This paper proposes an agent community based information retrieval method, which uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information related to a user query, an agent uses two histories : a query/retrieved (...) document history(Q/RDH) and a query/sender agent history(Q/SAH). The former is a list of pairs of a query and retrieved documents, where the queries were sent by the agent itself. The latter is a list of pairs of a query and sender agents and shows ``who sent what query to the agent''. This is useful to find a new information source. Making use of the Q/SAH is expected to cause a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads to perform a search. As an agent receives more queries, then more links to new knowledge are achieved. From this behavior, a ``give and take''(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agents Kodama which has been developed in our laboratory, and conducted preliminary experiments to test the hypothesis. The empirical results showed that the method was much more efficient than a naive method employing 'broadcast' techniques only to look up a target agent. (shrink)

This paper proposes the use of an argumentation framework with recursive attacks to address a trust model in a collaborative open multi-agent system. Our approach is focused on scenarios where agents share information about the credibility (informational trust) they have assigned to their peers. We will represent informants’ credibility through credibility objects which will include not only trust information but also the informant source. This leads to a recursive setting where the reliability of certain credibility (...) class='Hi'>information depends on the credibility of other pieces of information that should be subject to the same analysis. Credibility objects are maintained in a credibility base which can have information in conflict. In this scenario, we will formally show that our proposal will produce a partially ordered credibility relation; such relation contains the information that can be justified by an argumentation process. (shrink)

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, (...) the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology. (shrink)

Purpose Interdisciplinary linkage of information is an emerging topic to create knowledge by collaboration of experts in diverse domains. New insights can be found by using the combined techniques and information when people have the chance to discuss and communicate on a common basis. Design/methodology/approach This paper describes RMS Cloud, an information management system which allows distributed data sources to be searched using dynamic joins of results from heterogeneous data formats. It is based on the well-known Mediator (...) architecture, but reverses the connection of the data sources to grant data owners full control over the data. Findings Data owners and learners are enabled to retrieve information and to cross-connect domain-extrinsic knowledge and enhances collaborative learning with a search interface that is intuitive and easy to operate. Originality/value This novel architecture is able to connect to differently shaped data sources from interdisciplinary domains into one common retrieval interface. (shrink)

The objective of this work is to demonstrate how cooperative sharers and uncooperative free riders can be placed in different groups of an electronic society in a decentralised manner. We have simulated an agent-based open and decentralised P2P system which self-organises itself into different groups to avoid cooperative sharers being exploited by uncooperative free riders. This approach encourages sharers to move to better groups and restricts free riders into those groups of sharers without needing centralised control. Our approach is suitable (...) for current P2P systems that are open and distributed. Gossip is used as a social mechanism for information sharing which facilitates the formation of groups. Using multi-agent based simulations we demonstrate how the adaptive behaviour of agents lead to self-organisation. We have tested with varying the gossip level and checked its impact in the system’s behaviour. We have also investigated the impact of false gossip in this system where gossip is the medium for information sharing which leads to self-organisation. (shrink)

In this paper, we propose a framework for searching information through both the WWW and a human group. Though information retrieval using a searc engine in the WWW is very useful, we can not acquire local information owned by persons and not explicitly described in text. A user knows neither where target information is in the WWW nor who knows in a human group. Thus we integrate the information retrieval in the WWW with that in (...) a human group, develop a heterogeneous information search system HERIS as a multi-agent system. We develop this framework as multi-agent system consisting of user agents and search engine agents, and use contract net protocol to communication among the agents. A user agent and a search engine agent are assigned to a user and a search engine. Each of the agents maintains a user profile and a search engine profile. When a user gives a query to a system, it selectively searches information resources consisting the WWW and user group. Finally a user acquires integrated results of a hit list from the WWW and a audio/video connection to a user who can answer the query. It was found through experimental evaluation that information retrieval using a human group as an information resource is valid and a search with HERIS has a different character from conventional search engines. (shrink)

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse (...) planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration. (shrink)

This paper outlines a multi-agent architecture for regulated information exchange of crime investigation data between police forces. Interactions between police officers about information exchange are analysed as negotiation dialogues with embedded persuasion dialogues. An architecture is then proposed consisting of two agents, a requesting agent and a responding agent, and a communication language and protocol with which these agents can interact to promote optimal information exchange while respecting the law. Finally, dialogue policies are defined for the (...) individual agents, specifying their behaviour within a negotiation. Essentially, when deciding to accept or reject an offer or to make a counteroffer, an agent first determines whether it is obligatory or permitted to perform the actions specified in the offer. If permitted but not obligatory, the agent next determines whether it is in his interest to accept the offer. (shrink)

In this paper, we describe a collaborative information retrieval method among personal repositorie and an implementation of the method on a personal agent framework. We propose a framework for personal agents that aims to enable the sharing and exchange of information resources that are distributed unevenly among individuals. The kernel of a personal agent framework is an RDF(resource description framework)-based information repository for storing, retrieving and manipulating privately collected information, such as documents the user read (...) and/or wrote, email he/she exchanged, web pages he/she browsed, etc. The repository also collects annotations to information resources that describe relationships among information resources and records of interaction between the user and information resources. Since the information resources in a personal repository and their structure are personalized, information retrieval from other users' is an important application of the personal agent. A vector space model with a personalized concept-base is employed as an information retrieval mechanism in a personal repository. Since a personalized concept-base is constructed from information resources in a personal repository, it reflects its user's knowledge and interests. On the other hand, it leads to another problem while querying other users' personal repositories; that is, simply transferring query requests does not provide desirable results. To solve this problem, we propose a query equalization scheme based on a relevance feedback method for collaborative information retrieval between personalized concept-bases. In this paper, we describe an implementation of the collaborative information retrieval method and its user interface on the personal agent framework. (shrink)

In this paper, we propose an inter-personal information sharing model among individuals based on personalized recommendations. In the proposed model, we define an information resource as shared between people when both of them consider it important --- not merely when they both possess it. In other words, the model defines the importance of information resources based on personalized recommendations from identifiable acquaintances. The proposed method is based on a collaborative filtering system that focuses on evaluations (...) from identifiable acquaintances. It utilizes both user evaluations for documents and their contents. In other words, each user profile is represented as a matrix of credibility to the other users' evaluations on each domain of interests. We extended the content-based collaborative filtering method to distinguish other users to whom the documents should be recommended. We also applied a concept-based vector space model to represent the domain of interests instead of the previous method which represented them by a term-based vector space model. We introduce a personalized concept-base compiled from each user's information repository to improve the information retrieval in the user's environment. Furthermore, the concept-spaces change from user to user since they reflect the personalities of the users. Because of different concept-spaces, the similarity between a document and a user's interest varies for each user. As a result, a user receives recommendations from other users who have different view points, achieving inter-personal information sharing based on personalized recommendations. This paper also describes an experimental simulation of our information sharing model. In our laboratory, five participants accumulated a personal repository of e-mails and web pages from which they built their own concept-base. Then we estimated the user profiles according to personalized concept-bases and sets of documents which others evaluated. We simulated inter-personal recommendation based on the user profiles and evaluated the performance of the recommendation method by comparing the recommended documents to the result of the content-based collaborative filtering. (shrink)

Recently, agent technologies have attracted a lot of interest as an emerging programming paradigm. With such agent technologies, services are provided through collaboration among agents. At the same time, the spread of mobile technologies and communication infrastructures has made it possible to access the network anytime and from anywhere. Using agents and mobile technologies to realize ubiquitous computing systems, we propose a new framework based on KODAMA and VPC. KODAMA provides distributed management mechanisms by using the concept of community and (...) communication infrastructure to deliver messages among agents without agents being aware of the physical network. VPC provides a method of defining peer-to-peer services based on agent communication with policy packages. By merging the characteristics of both KODAMA and VPC functions, we propose a new framework for ubiquitous computing environments. It provides distributed management functions according to the concept of agent communities, agent communications which are abstracted from the physical environment, and agent collaboration with policy packages. Using our new framework, we conducted a large-scale experiment in shopping malls in Nagoya, which sent advertisement e-mails to users' cellular phones according to user location and attributes. The empirical results showed that our new framework worked effectively for sales in shopping malls. (shrink)

Research on children interacting with each other encompasses a wide variety of specific research interest, including but not limited to a focus on language. In this introduction to an issue of Discourse Studies devoted to the contribution of peer talk to pragmatic development, we define ‘peer talk’ as an interdisciplinary field of inquiry and we critically review literature on the role of peer talk in children’s pragmatic development. We suggest that ‘peer talk’ as a field of (...) inquiry properly encompasses studies of the development of language through peer interaction as well as the ways in which peer talk functions in children’s co-construction of their social and cultural worlds. Research reviewed here suggests that peer talk may be a crucial site for pragmatic development, offering children a wide range of opportunities for mutual learning of interactive as well as linguistic skills. The evidence we review, which comes from the fields of language socialization, second language learning, and child-discourse, highlights two major contextual features of peer talk: its collaborative, multi-party, symmetrical participation structure; and its role in the co-constructed worlds of childhood culture. Finally, we call for research and theorizing that would bridge the gap between developmental and ethnographically-informed approaches to the study of child discourse, in order to ensure that children’s peer talk receives the attention due it as a major site for both the development of discourse skills and the creation of childhood culture. (shrink)

This paper outlines a multi-agent architecture for regulated information exchange of crime investigation data between police forces. Interactions between police officers about information exchange are analysed as negotiation dialogues with embedded persuasion dialogues. An architecture is then proposed consisting of two agents, a requesting agent and a responding agent, and a communication language and protocol with which these agents can interact to promote optimal information exchange while respecting the law. Finally, dialogue policies are defined for the (...) individual agents, specifying their behaviour within a negotiation. Essentially, when deciding to accept or reject an offer or to make a counteroffer, an agent first determines whether it is obligatory or permitted to perform the actions specified in the offer. If permitted but not obligatory, the agent next determines whether it is in his interest to accept the offer. (shrink)

This special issue of the Logic Journal of the IGPL includes revised and updated versions of the best work presented at the fourth edition of the workshop Formal Ap- proaches to Multi-Agent Systems, FAMAS'09, which took place in Turin, Italy, from 7 to 11 September, 2009, under the umbrella of the Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW). -/- Just like its predecessor, research reported in this FAMAS 2009 special issue is very much inspired by practical concerns. (...) This time the authors of all the five selected papers are concerned with knowledge and beliefs in multi-agent settings: How to create a group belief in a fair way from individual plausibility orderings? How to close gaps and resolve ambiguities in a tractable way, when information comes from multiple sources? How to reason about a spatial environment? How to compare the strengths of an agent's beliefs in a principled way? How to decide as efficiently as possible whether a given formula concerning group beliefs is valid? These questions and their answers lead to a multi-faceted and at the same time coherent special issue. We concisely introduce the five articles. (shrink)

We extend our general approach to characterizing information to multi-agent systems. In particular, we provide a formal description of an agent''s knowledge containing exactly the information conveyed by some (honest) formula . Only knowing is important for dynamic agent systems in two ways. First of all, one wants to compare different states of knowledge of an agent and, secondly, for agent a''s decisions, it may be relevant that (he knows that) agent b does not know more than (...) . There are three ways to study the question whether a formula can be interpreted as minimal information. The first method is semantic and inspects minimal models for (with respect to some information order on states). The second one is syntactic and searches for stable expansions, minimal with respect to some language *. The third method is a deductive test, known as the disjunction property. We present a condition under which the three methods are equivalent. Then, we show how to construct the order by collecting layered orders. Focusing on the multi-agent case we identify languages * for various orders , and show how they yield different notions of honesty for different multi-modal systems. We then provide several tools for studying honesty types and illustrate their usefulness on a number of examples, for three modal systems of particular interest. Finally, we relate the different notions of minimal knowledge, and describe possible patterns of honesty for these systems. (shrink)

The emergence of new technologies such as the Internet of things and the Cloud transforms the way we interact. Whether it be human to human interaction or human to machine interaction, the size of the networks keeps growing. As the networks get more complex nowadays with many interconnected components, it is necessary to develop distributed scalable algorithms so as to minimize the computation required in decision making in such large-scale systems. In this paper, we consider a setup where each agent (...) in the network updates its opinion by relying on its neighbors’ opinions. The information exchange between the agents is assumed to be mutual. The cluster consensus problem is investigated for networks represented by static or time-varying graphs. Joint and integral connectivity conditions are utilized to determine the number of clusters that are formed, as the interactions among the agents evolve over time. Finally, some numerical examples are given to illustrate the theoretical results. (shrink)

We argue that the notion of trust, as it figures in an ethical context, can be illuminated by examining research in artificial intelligence on multi-agent systems in which commitment and trust are modeled. We begin with an analysis of a philosophical model of trust based on Richard Holton’s interpretation of P. F. Strawson’s writings on freedom and resentment, and we show why this account of trust is difficult to extend to artificial agents (AAs) as well as to other non-human (...) entities. We then examine Margaret Urban Walker’s notions of “default trust” and “default, diffuse trust” to see how these concepts can inform our analysis of trust in the context of AAs. In the final section, we show how ethicists can improve their understanding of important features in the trust relationship by examining data resulting from a classic experiment involving AAs. (shrink)

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation (...) model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory. (shrink)

In view of the poor recommendation performance of traditional resource collaborative filtering recommendation algorithms, this article proposes a collaborative filtering recommendation model based on deep learning for art and MOOC resources. This model first uses embedding vectors based on the context of metapaths for learning. Embedding vectors based on the context of metapaths aggregate different metapath information and different MOOCs may have different preferences for different metapaths. Secondly, to capture this preference drift, the model introduces (...) an attention mechanism, which can improve the interpretability of the recommendation results. Then, by introducing the Laplacian matrix into the prior distribution of the hidden factor feature matrix, the relational network information is effectively integrated into the model. Finally, compared with the traditional model using the scoring matrix, the model in this article using text word vectors effectively alleviates the impact of data sparsity and greatly improves the accuracy of prediction. After analyzing the experimental results, compared with other algorithms, the resource collaborative filtering recommendation model proposed in this article has achieved better recommendation results, with good stability and scalability. (shrink)

The effectiveness of information retrieval technology in electronic discovery (E-discovery) has become the subject of judicial rulings and practitioner controversy. The scale and nature of E-discovery tasks, however, has pushed traditional information retrieval evaluation approaches to their limits. This paper reviews the legal and operational context of E-discovery and the approaches to evaluating search technology that have evolved in the research community. It then describes a multi-year effort carried out as part of the Text Retrieval Conference to (...) develop evaluation methods for responsive review tasks in E-discovery. This work has led to new approaches to measuring effectiveness in both batch and interactive frameworks, large data sets, and some surprising results for the recall and precision of Boolean and statistical information retrieval methods. The paper concludes by offering some thoughts about future research in both the legal and technical communities toward the goal of reliable, effective use of information retrieval in E-discovery. (shrink)

As many philosophers agree, the frame problem is concerned with how an agent may efficiently filter out irrelevant information in the process of problem-solving. Hence, how to solve this problem hinges on how to properly handle semantic relevance in cognitive modeling, which is an area of cognitive science that deals with simulating human's cognitive processes in a computerized model. By "semantic relevance", we mean certain inferential relations among acquired beliefs which may facilitate information retrieval and practical reasoning under (...) certain epistemic constraints, e. g., the insufficiency of knowledge, the limitation of time budget, etc. However, traditional approaches to relevance—as for example, relevance logic, the Bayesian approach, as well as Description Logic—have failed to do justice to the foregoing constraints, and in this sense, they are not proper tools for solving the frame problem/relevance problem. As we will argue in this paper, Non-Axiomatic Reasoning System (NARS) can handle the frame problem in a more proper manner, because the resulting solution seriously takes epistemic constraints on cognition as a fundamental theoretical principle. (shrink)

We extend our general approach to characterizing information to multi-agent systems. In particular, we provide a formal description of an agent's knowledge containing exactly the information conveyed by some (honest) formula φ. Only knowing is important for dynamic agent systems in two ways. First of all, one wants to compare different states of knowledge of an agent and, secondly, for agent a's decisions, it may be relevant that (he knows that) agent b does not know more than (...) φ. There are three ways to study the question whether a formula φ can be interpreted as minimal information. The first method is semantic and inspects 'minimal' models for φ (with respect to some information order ≤ on states). The second one is syntactic and searches for stable expansions, minimal with respect to some language $\scr{L}^{\ast}$ . The third method is a deductive test, known as the disjunction property. We present a condition under which the three methods are equivalent. Then, we show how to construct the order ≤ by collecting 'layered orders'. Focusing on the multi-agent case we identify languages $\scr{L}^{\ast}$ for various orders ≤, and show how they yield different notions of honesty for different multi-modal systems. We then provide several tools for studying honesty types and illustrate their usefulness on a number of examples, for three modal systems of particular interest. Finally, we relate the different notions of minimal knowledge, and describe possible patterns of honesty for these systems. (shrink)

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor. This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. (...) In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model. (shrink)

Collaborative filtering is being used within organizations and in community contexts for knowledge management and decision support as well as the facilitation of interactions among individuals. This article analyzes rhetorical and technical efforts to establish trust in the constructions of individual opinions, reputations, and tastes provided by these systems. These initiatives have some important parallels with early efforts to support quantitative opinion polling and construct the notion of “public opinion.” The article explores specific ways to increase trust in (...) these systems, albeit a “guarded trust” in which individuals actively seek information about system foibles and analyze the reputations of participants. (shrink)

We explore whether digital inclusion, a public policy designed to provide high-speed internet infrastructure for historically digitally excluded populations, is associated with the social and ethical challenge of financial inclusion. Using evidence from a sizable P2P lender in the U.S., we document that digital inclusion is positively associated with P2P lending penetration and that this relation is more pronounced in counties with limited commercial bank loan penetration and higher minority populations. Our new evidence from cross-sectional tests suggests that digital inclusion (...) plays a key role in financial inclusion, particularly in regions with more vulnerable and/or underserved populations. In consequence tests, we document that high-risk borrowing is less likely to be denied in counties with higher digital inclusion and that digital inclusion is positively associated with P2P lending efficiency in the form of more repeated borrowing, decreased funding time, and improved funding fulfillment. In addition, we show that the availability of alternative information, a plausible channel through which digital inclusion is related to financial inclusion, is positively associated with efficiency in P2P lending. Our findings indicate that digital inclusion can empower financial service providers and other stakeholders to collaboratively fulfill their ethical and social responsibilities to meet the financial needs of historically marginalized groups. (shrink)

One of the major problems that artificial intelligence needs to tackle is the combination of different and potentially conflicting sources of information. Examples are multi-sensor fusion, database integration and expert systems development. In this paper we are interested in the aggregation of propositional logic-based information, a problem recently addressed in the literature on information fusion. It has applications in multi-agent systems that aim at aggregating the distributed agent-based knowledge into an (ideally) unique set of propositions. (...) We consider a group of autonomous agents who individually hold a logically consistent set of propositions. Each set of propositions represents an agent’s beliefs on issues on which the group has to make a collective decision. To make the collective decision, several aggregation procedures have been proposed in the literature. Assuming that all propositions in question are factually right or wrong, we ask how good belief fusion is as a truth tracker. Will it single out the true set of propositions? And how does information fusion compare with other aggregation procedures? We address these questions in a probabilistic framework and show that information fusion does especially well for agents with a middling competence of hitting the truth of an individual proposition. (shrink)

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule (...) based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. (shrink)

We give a model for iterated belief change in multi-agent systems. The formal tool we use for this is a combination of modal and dynamic logic. Two core notions in our model are the expansion of the knowledge and beliefs of an agent, and the processing of new information. An expansion is defined as the change in the knowledge and beliefs of an agent when it decides to believe an incoming formula while holding on to its current propositional (...) beliefs. To prevent our agents from forming inconsistent beliefs they do not expand with every piece of information they receive. Instead, our agents remember their original beliefs and every piece of information they receive. After every receipt of information they decide which subset of the received information should be incorporated into their original beliefs. This procedure is called the processing of new information. We show that our model of belief update behaves in an intuitive way and that it is not sensitive to criticism on comparable models. (shrink)

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware (...) recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP. The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated. (shrink)

This work formalizes an informant-based structured argumentation approach in a multi-agent setting, where the knowledge base of an agent may include information provided by other agents, and each piece of knowledge comes attached with its informant. In that way, arguments are associated with the set of informants corresponding to the information they are built upon. Our approach proposes an informant-based notion of argument strength, where the strength of an argument is determined by the credibility of its informant (...) agents. Moreover, we consider that the strength of an argument is not absolute, but it is relative to the resolution of the conflicts the argument is involved in. In other words, the strength of an argument may vary from one context to another, as it will be determined by comparison to its attacking arguments (respectively, the arguments it attacks). Finally, we equip agents with the means to express reasons for or against the consideration of any piece of information provided by a given informant agent. Consequently, we allow agents to argue about the arguments’ strength through the construction of arguments that challenge (respectively, defeat) or are in favour of their informant agents. (shrink)

In this article, we compare two fixed-point constructions of common knowledge in sequential coordination problems. The first one corresponds to the standard maximal fixed-point construction of common knowledge in a possible worlds framework; the second construction provides an even larger fixed-point and involves iterated epistemic updates. We call the first fixed-point the maximal fixed-point, and the second the update fixed-point. Both fixed-points define a set of sequential actions that solve the coordination problem such that success is mutually guaranteed. The main (...) application intended is the study of signalling games as introduced by [15]. We introduce a non-probabilistic version of these games as multi-agent signalling games. We use them for modelling prototypical accommodation phenomena following [16]. We show especially that accommodation, which involves presupposition violation, is only licensed due to the existence of the super large update fixed-point, whereas the literal language use without presupposition violations can be characterized by the maximal fixed-point. (shrink)

In this paper, we consider how an agent may manage a questioning agenda in a situation where multiple information sources are available. We work within the framework of formal dialogue systems with the underpinning of Inferential Erotetic Logic. Firstly, we present the formal dialogue system DL(IEL)mult for managing multi-agent information retrieval. Then, we extend the proposed system so that it is capable of representing group and individual levels for the question decomposition process. We also propose two measures (...) for evaluating information sources: their cooperativeness and success levels; next, we analyse how the choice of agents may influence the way in which a solution for a given problem is reached. (shrink)

It is now more than ten years since the EU FET project ALFEBIITE finished, during which its researchers made original and distinctive contributions to (inter alia) formal models of trust, model-checking, and action logics. ALFEBIITE was also a highly inter-disciplinary project, with partners from computer science, philosophy, cognitive science and law. In this paper, we reflect on the interaction between computer scientists and information and IT lawyers on the idea of the ‘open agent society’. This inspired a programme of (...) research whose investigation into conceptual challenges has carried it from the logical specification of agent societies and dynamic norm-governed systems to self-organising electronic institutions, while developing several technologies for agent-based modelling and complex event recognition. The outcomes of this inter-disciplinary collaboration have also influenced current research into using the open agent society as a platform for socio-technical systems, and other collective adaptive systems. We present a number of research challenges, including the ideas of computational justice and polycentric governance, and explore a number of ethical, legal and social implications. We contend that, in order to address these issues and challenges, the continued inter-disciplinary collaboration between computer science and IT lawyers is critical. (shrink)

When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...) the part of some researchers that algorithms can also be biased. Here we illustrate the point that algorithms themselves can be the source of bias with the example of collaborative filtering algorithms for recommendation and search. These algorithms are known to suffer from cold-start, popularity, and homogenizing biases, among others. While these are typically described as statistical biases rather than biases of moral import; in this paper we show that these statistical biases can lead directly to discriminatory outcomes. The intuitive idea is that data points on the margins of distributions of human data tend to correspond to marginalized people. The statistical biases described here have the effect of further marginalizing the already marginal. Biased algorithms for applications such as media recommendations can have significant impact on individuals’ and communities’ access to information and culturally-relevant resources. This source of bias warrants serious attention given the ubiquity of algorithmic decision-making. (shrink)

Argument evaluation, one of the central problems in argumentation theory, consists in studying what makes an argument a good one. This paper proposes a formal approach to argument evaluation from the perspective of justification logic. We adopt a multi-agent setting, accepting the intuitive idea that arguments are always evaluated by someone. Two general restrictions are imposed on our analysis: non-deductive arguments are left out and the goal of argument evaluation is fixed: supporting a given proposition. Methodologically, our approach uses (...) several existing tools borrowed from justification logic, awareness logic, doxastic logic and logics for belief dependence. We start by introducing a basic logic for argument evaluation, where a list of argumentative and doxastic notions can be expressed. Later on, we discuss how to capture the mentioned form of argument evaluation by defining a preference operator in the object language. The intuitive picture behind this definition is that, when assessing a couple of arguments, the agent puts them to a test consisting of several criteria. As a result of this process, a preference relation among the evaluated arguments is established by the agent. After showing that this operator suffers a special form of logical omniscience, called preferential omniscience, we discuss how to define an explicit version of it, more suitable to deal with non-ideal agents. The present work exploits the formal notion of awareness in order to model several informal phenomena: awareness of sentences, availability of arguments and communication between agents and external sources. We discuss several extensions of the basic logic and offer completeness and decidability results for all of them. (shrink)

In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation (...) algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online. (shrink)

In this paper, we will address anonymity, privacy and trust issues that arise during the research on a communication platform for multi-modal transport. Though most logistic information is currently available in electronic form, it is not widely accessible yet to all the parties concerned with transport. The major goal of a communication platform is to improve the conditions for exchanging information, which should lead to better organisation/collaboration within the transport sector. We need to merit credibility by faithfully (...) modelling all the relevant real world interactions. Therefore, we opted for a connectivity solution in which software agents act as representatives of the parties involved. All agents can be equipped with human-like skills and qualities such as intelligence, autonomy, and the ability to cooperate, coordinate and negotiate. We will demonstrate how cooperation between different transport parties can be achieved without necessarily putting all the sensitive information on the web. (shrink)

Background: The concept of dignity can be divided into two main attributes: absolute dignity that calls for recognition of an inner worth of persons and social dignity that can be changeable and can be lost as a result of different social factors and moral behaviours. In this light, the nursing profession has a professional dignity that is to be continually constructed and re-constructed and involves both main attributes of dignity. Objectives: The purpose of this study was to determine how nurses (...) described nursing’s professional dignity in internal medicine and surgery departments in hospital settings. Research design: The research design was qualitative. Ethical considerations: This study was approved by the ethics committees of the healthcare organizations involved. All the participants were provided with information about the purpose and the nature of the study. Participants: A total of 124 nurses participated in this study. Method: The data were collected using 20 focus group sessions in different parts of Italy. The data were analysed by means of a conventional inductive content analysis starting from the information retrieved in order to extract meaning units and sorting the arising phenomena into conceptually meaningful categories and themes. Results: Nursing’s professional dignity was deeply embedded in the innermost part of individuals. Regarding the social part of dignity, a great importance was put on the values that compose nursing’s professional identity, the socio-historical background and the evolution of nursing in the area considered. The social part of dignity was also linked to collaboration with physicians and with healthcare assistants who were thought to have a central role in easing work strain. Equally important, though, was the relationship with peers and senior nurses. Conclusion: The organizational environments under scrutiny with their low staffing levels, overload of work and hierarchical interactions did not promote respect for the dignity of nurses. To understand these professional values, it is pivotal to comprehend the role of different health professions in their cultural milieu and the evolution of the nursing profession in diverse countries. (shrink)

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this (...) approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest. (shrink)

Compositional verification aims at managing the complexity of theverification process by exploiting compositionality of the systemarchitecture. In this paper we explore the use of a temporal epistemiclogic to formalize the process of verification of compositionalmulti-agent systems. The specification of a system, its properties andtheir proofs are of a compositional nature, and are formalized within acompositional temporal logic: Temporal Multi-Epistemic Logic. It isshown that compositional proofs are valid under certain conditions.Moreover, the possibility of incorporating default persistence ofinformation in a system, (...) is explored. A completion operation on aspecific type of temporal theories, temporal completion, is introducedto be able to use classical proof techniques in verification withrespect to non-classical semantics covering default persistence. (shrink)

The papers collected in this volume exemplify some of the trends in current approaches to logic, language and computation. Written by authors with varied academic backgrounds, the contributions are intended for an interdisciplinary audience. The first part of this volume addresses issues relevant for multi-agent systems: reasoning with incomplete information, reasoning about knowledge and beliefs, and reasoning about games. Proofs as formal objects form the subject of Part II. Topics covered include: contributions on logical frameworks, linear logic, and (...) different approaches to formalized reasoning. Part III focuses on representations and formal methods in linguistic theory, addressing the areas of comparative and temporal expressions, modal subordination, and compositionality. (shrink)

In this paper we propose an approach to multi-source belief revision where the trust or credibility assigned to informant agents can be revised. In our proposal, the credibility of each informant represented as a strict partial order among informant agents, will be maintained in a repository called credibility base. Upon arrival of new information concerning the credibility of its peers, an agent will be capable of revising this strict partial order, changing the trust assigned to its peers accordingly. (...) Our goal is to formalize a set of change operators over the credibility base: expansion, contraction, prioritized, and non-prioritized revision. These operators will provide the capability of dynamically modifying the credibility of informants considering the reliability of the information. This dynamics will reflect a new perception of trust assigned to the informant, or extend the set of informants by admitting the addition of new informant agents. (shrink)

In this paper, it is shown how formal dialectic can be extended to model multi-agent argumentation in which each participant is an agent. An agent is viewed as a participant in a dialogue who not only has goals, and the capability for actions, but who also has stable characteristics of types that can be relevant to an assessment of some of her arguments used in that dialogue. When agents engage in argumentation in dialogues, each agent has a credibility function (...) that can be adjusted upwards or downwards by certain types of arguments brought forward by the other agent in the dialogue. One type is the argument against the person or argumentum ad hominem, in which personal attack on one party's character is used to attack his argument. Another is the appeal to expert opinion, traditionally associated with the informal fallacy called the argumentum ad verecundiam. In any particular case, an agent will begin a dialogue with a given degree of credibility, and what is here called the credibility function will affect the plausibility of the arguments put forward by that agent. In this paper, an agent is shown to have specific character traits that are vital to properly judging how this credibility function should affect the plausibility of her arguments, including veracity, prudence, sincerity and openness to opposed arguments. When one of these traits is a relevant basis for an adjustment in a credibility function, there is a shift to a subdialogue in which the argumentation in the case is re-evaluated. In such a case, it is shown how the outcome can legitimately be a reduction in the credibility rating of the arguer who was attacked. Then it is shown how the credibility function should be brought into an argument evaluation in the case, yielding the outcome that the argument is assigned a lower plausibility value. (shrink)

A semantics is presented for belief-revision in the face of common announcements to a group of agents that have beliefs about each other's beliefs. The semantics is based on the idea that possible worlds can be viewed as having an internal structure, representing the belief independent features of the world, and the respective belief states of the agents in a modular fashion. Modularity guarantees that changing one aspect of the world (a belief independent feature or a belief state) has no (...) effect on any other aspect of the world. This allows us to employ an AGM-style selection function to represent revision. The semantics is given a complete axiomatisation (identical to the axiomatisation found by Gerbrandy and Groeneveld for a semantics based on non-wellfounded set theory) for the special case of expansion. (shrink)

A semantics is presented for belief revision in the face of common announcements to a group of agents that have beliefs about each other’s beliefs. The semantics is based on the idea that possible worlds can be viewed as having an internal-structure, representing the belief independent features of the world, and the respective belief states of the agents in a modular fashion. Modularity guarantees that changing one aspect of the world (a belief independent feature or a belief state) has no (...) effect on any other aspect of the world. This allows us to employ an AGM-style selection function to represent revision. The semantics is given a complete axiomatisation (identical to the axiomatisation found by Gerbrandy and Groeneveld for a semantics based on non-wellfounded set theory) for the special case of expansion. (shrink)

Do children derive different benefits from group collaboration at different ages? In the present study, 183 children from two age groups took part in a class quiz as members of a group, or individually. In some groups, cohesiveness was made salient by awarding prizes to the top performing groups. In other groups, prizes were awarded to the best performing individuals. Findings, both in terms of social outcomes and performance in the quiz, indicated that the 8-year olds viewed the benefits of (...) group membership in terms of the opportunities to receive information from other members. The 13-year olds, in contrast, viewed group collaboration as a constructive process where success was connected with group cohesiveness. (shrink)

One of the challenging problems in large scale cyber-argumentation platforms is that users often engage and focus only on a few issues and leave other issues under-discussed and under-acknowledged. This kind of non-uniform participation obstructs the argumentation analysis models to retrieve collective intelligence from the underlying discussion. To resolve this problem, we developed an innovative opinion prediction model for a multi-issue cyber-argumentation environment. Our model predicts users’ opinions on the non-participated issues from similar users’ opinions on related issues using (...) intelligent argumentation techniques and a collaborative filtering method. Based on our detailed experimental results on an empirical dataset collected using our cyber-argumentation platform, our model is 21.7% more accurate, handles data sparsity better than other popular opinion prediction methods. Our model can also predict opinions on multiple issues simultaneously with reasonable accuracy. Contrary to existing opinion prediction models, which only predict whether a user agrees on an issue, our model predicts how much a user agrees on the issue. To our knowledge, this is the first research to attempt multi-issue opinion prediction with the partial agreement in the cyber-argumentation platform. With additional data on non-participated issues, our opinion prediction model can help the collective intelligence analysis models to analyze social phenomena more effectively and accurately in the cyber argumentation platform. (shrink)

This paper focuses on the Enkratic principle of rationality, according to which rationality requires that if an agent sincerely and with conviction believes she ought to X, then X-ing is a goal in her plan. We analyze the logical structure of Enkrasia and its implications for deontic logic. To do so, we elaborate on the distinction between basic and derived oughts, and provide a multi-modal neighborhood logic with three characteristic operators: a non-normal operator for basic oughts, a non-normal operator (...) for goals in plans, and a normal operator for derived oughts. We prove two completeness theorems for the resulting logic, and provide a dynamic extension of the logic by means of product updates. We illustrate how this setting informs deontic logic by considering issues related to the filtering of inconsistent oughts, the restricted validity of deontic closure, and the stability of oughts and goals under dynamics. (shrink)

Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval protocols that use peer nodes to (...) preserve privacy. Previously, we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data. (shrink)

Beyond Interdisciplinarity examines the broadening meaning of core concept across academic disciplines and other forms of knowledge. In this book, Associate Editor of The Oxford Handbook of Interdisciplinarity and internationally recognized scholar Julie Thompson Klein depicts the heterogeneity and boundary work of inter- and trans-disciplinarity in a conceptual framework based on an ecology of spatializing practices in transaction spaces, including trading zones and communities of practice. The book includes both "crossdisciplinary" work (encompassing multi-, inter-, and trans-disciplinary forms) as well (...) as "cross-sector" work (spanning disciplines, fields, professions, government and industry, and communities). The first section of the book defines and explains boundary work, discourses of interdisciplinarity, and the nature of interdisciplinary fields. In the second section, Klein examines dynamics of working across disciplines, including communication, collaboration, and learning with concrete examples and lessons from research projects and programs that transcend traditional fields. The closing chapter examines reasons for failure and success then presents gateways to literature and other resources. Throughout the book, Klein emphasizes the roles of contextualization and historical change while factoring in the shifting relationship of disciplinarity and interdisciplinarity, ascendancy of transdisciplinarity, and intersections with other constructs including Mode 2 knowledge production, convergence, team science, and postdisciplinarity. The conceptual framework she provides also includes the role of boundary objects, agents, and organizations in brokering differences and creating for platforms for change. Klein further explains why translation, interlanguage, and a communication boundary space are vital to achieving intersubjectivity and collective identity. They foster not only pragmatics of negotiation and integration but also reflexivity, transactivity, and co-production of knowledge with stakeholders beyond the academy. Rhetorics of holism and synthesis compete with instrumentalities of problem solving and transgressive critiques. However, typical warrants today include complexity, contextualization, collaboration, and socially-robust knowledge. Crossing boundaries remains complex, but this book guides readers through the density of pertinent literature while expanding understandings of crossdisciplinary and cross-sector work. (shrink)