In this paper, we propose a learning method for an agent to interact with other agents effectively. This method, MULA-C, improves efficiency of the learning, by clustering agents, and influences the learning experience of one agent to other agents which belong to the same cluster. Similarity among agents is evaluated by similarity among Q-values of agents. We give the detail explanation of learning method of MULA-C, and present the result of experiments which shows the effectiveness of MULA-C.

In the study of automatic summarization, the main research topic was `important sentence extraction' but nowadays `sentence compression' is a hot research topic. Conventional sentence compression methods usually transform a given sentence into a parse tree or a dependency tree, and modify them to get a shorter sentence. However, this method is sometimes too rigid. In this paper, we regard sentence compression as an combinatorial optimization problem that extracts an optimal subsequence of words. Hori et al. also proposed a similar (...) method, but they used only a small number of features and their weights were tuned by hand. We introduce a large number of features such as part-of-speech bigrams and word position in the sentence. Furthermore, we train the system by discriminative learning. According to our experiments, our method obtained better score than other methods with statistical significance. (shrink)

This paper discusses dynamic properties of human communications networks, which appears as a result of informationexchanges among people. We propose agent-based simulation to examine implicit mechanisms behind the dynamics. The ABS enables us to reveal the characteristics and the differences of the networks regarding the specific communicationgroups. We perform experiments on the ABS with activity data from questionnaires survey and with virtual data which isdifferent from the activity data. We compare the difference between them and show the effectiveness of the (...) ABS through theexperiments. (shrink)

Recently, e-learning systems for self-learning with various types of retrieval functions have been developed. This paper describes a method for keyword extraction using retrieval information stored from many students through the retrieval functions. Firstly, we show that teachers tend to consider technical terms as important, while students unfamiliar with the technical terms tend to retrieve the terms, therefore there is a clear correlation between keywords extracted by the teachers and the retrieval words by the students. Secondly, we propse a method (...) utilizing retrieval information from the students for keyword extraction, and show that the mehod can achieve quite better performance than a method extracting keywords using only lecture information. (shrink)

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a kernel density estimator. We also propose an application of crossover kernels (...) to Expectation-Maximization estimation (EM) of Gaussian mixtures. (shrink)

Information compilation is a novel technology that allows it to compile various information intelligently, and to make it easy to understand and to access. In this paper, as an instance of the possibilities of information compilation, we show a framework that extracts and visualizes given time-series information and its changes, and provides users with a multi-modal summarization and also an interactive interface for accessing that information. It can meet information requests, in which users need to comprehend some trend and movement, (...) and access a series of documents containing specific time-series information related. We emphasis the importance of changes of data during some time period rather than data points, as the unit of information extracted and represented. Based on this idea, we propose a visualization method in which qualitative and quantitative characteristics of changes of a given time-series information are plotted with textually represented comments, and a widely applicable information extraction method that regards the changes of time-series information as information primitives and extracts those for the visualization. (shrink)

One of the main difficulties in motor skill acquisition is attributed to body control based on wrong mental models. This is true to various domains such as playing sports and playing musical instruments. In order to acquire adequate motor skill by modifying false belief, we need to help people find appropriate key points in achieving a body control and integrate them. In this paper, we investigate three approaches to realize such support. The first one is to encourage exploration of the (...) relations among key points constituting a motor skill, using a technique of meta-cognitive verbalization. The second one is to represent a motor skill by appropriate mechanical models. The third one is to integrate rules for component tasks in achieving a compound task. These three approaches, we argue, help people build an integrated mental model consisting of multiple relations among various key points, one that seems to be indispensable for acquisition of motor skills. These ideas suggest the possibility to create new skill rules to perform difficult tasks automatically. (shrink)

An agent organization system for multi-agent based network management, called ARTISTE, is proposed. For a multi-agent system to solve problems effectively, it is important to organize agents appropriately. Organizing agents adaptively on the Internet, however, is not easy, because the status of the Internet changes dynamically in a short time and no one can have a complete view of the whole network. The aims of ARTISTE are to form an agent organization in accordance with the current Internet and its problem-solving (...) context and to provide organizational information for target MASs. ARTISTE operates as an independent system with respect to any MAS. To organize agents, ARTISTE collects information about agents' abilities and statuses, network information such as topologies, and a problem-dependent requirement from a target MAS. ARTISTE is designed as an MAS, and it can collect the information about the network and the target MAS from multiple observation points. Furthermore, ARTISTE agents exchange their own local information in order to create a more global view of the network and the distribution of the agents. Our performance evaluation showed that ARTISTE works with sufficient performance for use in the actual Internet environment. (shrink)

Recently we proposed a method of compiling prioritized circumscription into answer set programming. However, its encoding has the guess and check structure, where the candidates are generated by the guess program and the check is expressed by the inconsistency of the check program. In this paper, we present another method which compiles prioritized circumscription into a single general disjunctive program by means of integration of the guess and check programs. The answer sets of the transformed GDP yield models of a (...) given circumscription by emulating the inconsistency of the check program using integrity constraints. Thanks to our integration technique, the circumscriptive theorem prover to evaluate a query with respect to prioritized circumscription is easily established by expressing the inconsistency check in a single GDP. Our experimental results show that the performance of the tool we have implemented using our new method has not only improved remarkably, i.e. reduction of runtime as compared to our previous method, but has also exceeded that of the recently developed software tool, prio_circ2dlp, for prioritized circumscription as far as our experiments are concerned. (shrink)

An autonomous agent in the real world should learn its own sensor-motor coordination through interactions with the environment; otherwise the behaviors can not be grounded and they can easily be inappropriate in the variety of the environment. The sensor-motor signals are usually complex time sequence, therefore the cognitive action system of the agent has to handle them.In this paper, we propose a computational model of the cognitive action system that consists of a sensor space HMM-SOM, a motor space HMM-SOM and (...) connection mapping between the two HMM-SOMs. A HMM-SOM can be recognized as a set of HMMs that are placed in a SOM space. It can model a set of complex time series signal in a self-organizing manner.We apply this HMM-SOM based cognitive action system on vision-motion and auditory-articulation signals. The experimental results show that this system is basically capable of constructing sensor-motor coordination structure in a self-organizing manner, handling complex time series signals. (shrink)

In this paper, we analyze the effect of the speed on the task of random number generation. Each subjectgenerates the sequences of ``random'' numbers under various speed conditions. In addition to the 16 indiceswhich have been used in the past studies, we introduce and calculate the new indices called the ``frequency ofschema''. From the statistical analysis, we find that there is an obvious change, along with the speed, in theproperties of sequences, indicating the transition of the underling information processing. It (...) is shown that thefrequencies of schemas are effective indices for describing the properties of sequences produced under high-speed conditions. (shrink)

Function optimization underlies many real-world problems and hence is an important research subject. Most of the existing optimization methods were developed to solve primarily unconstrained problems. Since real-world problems are often constrained, appropriate handling of constraints is necessary in order to use the optimization methods. In particular, the performances of some methods such as Genetic Algorithms can be substantially undermined by ineffective constraint handling. Despite much effort devoted to the studies of constraint-handling methods, it has been reported that each of (...) them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed. For this reason, we investigated the guidelines for a method to effectively handle constraints. The guidelines are that the method 1) takes the approach of repair operators, 2) monotonically decreases both the number of violated constraints and constraint violations, and 3) searches over the boundaries of violated constraints. Based on these guidelines, we designed a new constraint-handling method Pareto Descent Repair operator in which ideas derived from multi-objective local search and gradient projection method are incorporated. Experiments comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR. (shrink)

The mammalian immune system is a subject of great research interest because of its powerful information processing capabilities, namely, adaptivity. The adaptivity of the immune system is characterized by mainly two aspects, responsibility and diversity. The responsibility is a result of the response network of the immune system and the diversity is arise from gene rearrengement of the immune system. Recentry many artificial immune algorithms were devised by inspiring the adaptivity of the immune system. In terms of the two aspects (...) of the immune system, however, those artificial immune algorithms only utilize the responsibility using models of response and regulation networks in the immune system. This paper proposes a new scheme of artificial immune algorithm, called Rearrangement Immune Algorithm, in which the rearrengement of the immune system is utilized combining evolution of the gelmline with an optimization of genetic algorithms. We empirically shows the effectiveness of our new scheme, RIA, with applying the Rosenbrock function and an HP folding problem. (shrink)

In this paper, we investigate distinctive utterances in non-task-oriented conversational dialogue through the comparison between task-oriented dialogue and non-task-oriented conversational dialogue. We then found that Indirect Responses and Clarification Requests are significant in non-task-oriented conversational dialogue. IRs are cooperative responses to other's question, while CRs are clarification questions. We analyzed the rhetorical relations about IRs and CRs. We then found that the IRs are generated by evidence and causal relations, while the CRs are generated by elaboration relation and causal relations.

In this paper, we propose a multiagent model for wide-area disaster-evacuation simulations with local factorsconsidered. Conventional multiagent models for evacuation simulations neither allow general-purpose computers to executewide-area simulations nor allow the object area to be changed easily. If these problems are solved, however, these simulations can be useful for local governments to make disaster damage prevention plan. In the proposed model, each roadis expressed by a series of cells. A computational amount relevant to interaction among agents is reduced by describing (...) themodel for agents to be affected by other agents through the state of each cell. This makes possible wide area simulations. Using the data of a digital map database that is widely used for car navigation systems enables the simulations to beperformed for any region in Japan. Local factors are reflected in simulations by setting some parameters for evacuees, anevacuation environment, and disaster damage prevention plan of the object area. As an evaluation experiment, wesimulated the situations of Kobe city on the date of the Great Hanshin-Awaji Earthquake. Simulations results about thepercentage of evacuees who arrived at refuges were in good agreement with the actual data when parameters forevacuation-start timing were adjusted. We also simulated the current situations of two cities, Kobe and Tsukuba, and confirmed that this model was successfully applied to the two cities. From these evaluation experiments, we believe thatthis model can be applied to various areas and will perform further experiments in the future. (shrink)

This paper discusses DNA-based stochastic optimizations under the constraint that the search starts from a given point in a search space. Generally speaking, a stochastic optimization method explores a search space and finds out the optimum or a sub-optimum after many cycles of trials and errors. This search process could be implemented efficiently by ``molecular computing'', which processes DNA molecules by the techniques of molecular biology to generate and evaluate a vast number of solution candidates at a time. We assume (...) the exploration starting from a single point, and propose a method to embody DNA-based optimization under this constraint, because this method has a promising application in the research field of protein engineering. In this application, a string of nucleotide bases encodes a protein possessing a specific activity, which could be given as a value of an objective function. Thus, a problem of obtaining a protein with the optimum or a sub-optimum about the desired activity corresponds to a combinatorial problem of obtaining a base sequence giving the optimum or a sub-optimum in the sequence space. Biologists usually modify a base sequence corresponding to a naturally occurring protein into another sequence giving a desired activity. In other words, they explore the space in the proximity of a natural protein as a start point. We first examined if the optimization methods that involve a single start point, such as simulated annealing, Gibbs sampler, and MH algorithms, can be implemented by DNA-based operations. Then, we proposed an application of genetic algorithm, and examined the performance of this application on a model fitness landscape by computer experiments. These experiments gave helpful guidelines in the embodiments of DNA-based stochastic optimization, including a better design of crossover operator. (shrink)

Though many kinds of multi-agent systems based on argumentation have been proposed where only rule-based knowledge is taken into account, they have been unable to handle the ontological knowledge so far. In our daily life, however, there are a lot of human argumentation where both ontological and rule knowledges are used. For example, in e-commerce, a seller and a buyer usually use ontologies about products along with their respective strategic rules for buying and selling. Recent progress of the Semantic Web (...) technology provides expressive ontology languages. In this paper, we demonstrate integration of the Semantic Web reasoning and argument-based reasoning. We have implemented the integrated system such that Logic of Multiple-valued Argumentation-based agent system (specialized to two values { f, t }) can be accessible to the Semantic Web reasoning established as the description logic reasoning system, given ontologies expressed by OWL DL or its notational variant the DL SHOIN (D). An interesting argumentation result using both ontologies and rules about the university curriculum is shown as an example executed by our system. (shrink)

Covisibility-based mapping is a paradigm for robotic map building research in which a mobile robot estimates multiple object positions only from ``covisibility'' information, i.e., ``which objects were recognized at a time''. In previous studies on this problem, a solution based on a combination of heuristics - ``closely located objects are likely to be seen simultaneously more often than distant objects'' and Multi-Dimensional Scaling (MDS) was proposed, and it was shown that qualitative spatial relationships among objects are learned with high accuracy (...) by this method. However, theoretical validity of the heuristics has not been sufficiently discussed in these studies. Besides, the existing method has a defect that the quantitative accuracy of built maps is very low. In this paper, we first prove that the heuristics is generally valid in a certain condition, and then present several enhancements to the original method in order to improve the quantitative accuracy of the maps. In the experiments, it was found these enhacements are quite effective. (shrink)

In recent years, simultaneous localization and mapping based on stochastic state-transition / observation models and Bayesian estimation technique has been the mainstream of the mobile robot mapping research. In contrast to this trend, we present an alternative formulation of the map building problem from the viewpoint of non-linear dimensionality reduction or manifold learning. In this framework, the robot map building is interpreted as a problem of reconstructing the coordinates of objects so that proximities between them in the space of robot's (...) observation history as faithfully as possible. Based on this insight, we generalize the covisibility-based mapping method which was established in previous studies into the map building based on dimensionality reduction of historical visibility data. We applied latest non-linear dimensionality reduction techniques to this framework, and compared them with classical techniques such as PCA and MDS in experimental studies. (shrink)

Recently, we proposed an agent-based model called the word of mouth model to analyze the influence of an information transmission process to price formation in financial markets. Especially, the short-term predictability of asset return was focused on and an explanation in the view of information transmission was provided to the question why the predictability was much clearly observed in the small-sized stocks. This paper, to extend the previous study, demonstrates that the word of mouth model also has a consistency with (...) other important financial stylized facts. This strengthens the possibility that the information transmission among investors plays a crucial role in price formation. Concretely, this paper addresses two famous statistical features of returns; the leptokurtic distribution of return and the autocorrelation of return volatility. The reasons why these statistical facts receive especial attentions of researchers among financial stylized facts are their statistical robustness and practical importance, such as the applications to the derivative pricing problems. (shrink)

Unsupervised learning techniques, e.g. clustering, is useful for obtaining a summary of a dataset. However, its application to large databases can be computationally expensive. Alternatively, useful information can also be retrieved from its subsets in a more efficient yet effective manner. This paper addresses the problem of finding a small subset of minority instances whose distribution significantly differs from that of the majority. Generally, such a subset can substantially overlap with the majority, which is problematic for conventional estimation of distribution. (...) This paper proposes a new approach for estimating a minority distribution based on Information Theoretic Framework, an extension of the Rate Distortion Theory for unsupervised learning tasks. Specifically, the proposed method estimates parameters which maximize the divergence between the minority and majority distributions, penalizes the redundancy of data expression based on the mutual information between the observed and hidden variables, and employs a hard assignment approximation to avoid computation of trivial conditional probabilities. The algorithm of the proposed method has no problem-dependent parameter and its time and space complexities are linear to the size of the minority subset. Experiments using artificial datasets show the proposed method yields significantly high precision and sensitivity in detecting minority subsets which substantially overlaps with the majority. The proposed method also substantially outperforms one-class classification and mixture estimation methods in real-world benchmark datasets for text and satellite imagery classification. (shrink)

Reinforcement Learning is a kind of machine learning. We know Profit Sharing, the Rational Policy Making algorithm, the Penalty Avoiding Rational Policy Making algorithm and PS-r* to guarantee the rationality in a typical class of the Partially Observable Markov Decision Processes. However they cannot treat continuous state spaces. In this paper, we present a solution to adapt them in continuous state spaces. We give RPM a mechanism to treat continuous state spaces in the environment that has the same type of (...) a reward. We show the effectiveness of the proposed method in numerical examples. (shrink)

In this research, we proposed multi-robot system that has robustness to the change of environments and has adaptability to the change of tasks and number of robots. To implement them, we use parameter learning on subsumption architecture.The subsumption architecture control robots' fundamental behaviors and robots move continuously in principle.The optimal value of the parameter and the number of robot that does the task in field are searched by learning. As the result,the robot's action on the task is improved.Proposed system has (...) functions to recognize the task and to generate the recordation memory.And then robots adapt to the change of tasks and the number of robots.In this paper, the function of proposed system is tested in simulation experiments. (shrink)

It has been recognized that design rationale is necessary for understanding and designing artifacts. In order to capture it, we focus on functional knowledge as design rationale and have developed a functional knowledge modeling tool, named SOFAST, which has been deployed in a few companies. In this paper, we aim to capture the processes related to functional knowledge which are not dealt with by the current SOFAST. We discuss a systematic description and an automatic capture system of two typical processes: (...) modification process of functional decomposition trees in the context of designer's daily jobs, and systematization processes in which functional knowledge grows. For the implementation of the system, we extended SOFAST using an ontology-based Knowledge Management framework. Through demonstration and trial use of the system, SOFAST's users positively evaluated the system's effectiveness and expectable usefulness for their daily work. (shrink)

This paper describes how to perform syntactic parsing and semantic analysis in a dialog system. The paper especially deals with how to disambiguate potentially ambiguous sentences using the contextual information. Although syntactic parsing and semantic analysis are often studied independently of each other, correct parsing of a sentence often requires the semantic information on the input and/or the contextual information prior to the input. Accordingly, we merge syntactic parsing with semantic analysis, which enables syntactic parsing taking advantage of the semantic (...) content of an input and its context. One of the biggest problems of semantic analysis is how to interpret dependency structures. We employ a framework for semantic representations that circumvents the problem. Within the framework, the meaning of any predicate is converted into a semantic representation which only permits a single type of predicate: an identifying predicate "aru". The semantic representations are expressed as sets of "attribute-value" pairs, and those semantic representations are stored in the context information. Our system disambiguates syntactic/semantic ambiguities of inputs referring to the attribute-value pairs in the context information. We have experimentally confirmed the effectiveness of our approach; specifically, the experiment confirmed high accuracy of parsing and correctness of generated semantic representations. (shrink)

We propose an efficient algorithm for deciding the reachability between any nodes on XML data represented by connected directed graphs. We develop a technique to reduce the size of the reference table for the reachability test. Using the small table and the standard range labeling method for rooted ordered trees, we show that our algorithm answers almost queries in a constant time preserving the space efficiency and a reasonable preprocessing time.

The number of competing-brands changes by new product's entry. The new product introduction is endemic among consumer packaged goods firm and is an integral component of their marketing strategy. As a new product's entry affects markets, there is a pressing need to develop market response model that can adapt to such changes. In this paper, we develop a dynamic model that capture the underlying evolution of the buying behavior associated with the new product. This extends an application of a dynamic (...) linear model, which is used by a number of time series analyses, by allowing the observed dimension to change at some point in time. Our model copes with a problem that dynamic environments entail: changes in parameter over time and changes in the observed dimension. We formulate the model with framework of a state space model. We realize an estimation of the model using modified Kalman filter/fixed interval smoother. We find that new product's entry (1) decreases brand differentiation for existing brands, as indicated by decreasing difference between cross-price elasticities; (2) decreases commodity power for existing brands, as indicated by decreasing trend; and (3) decreases the effect of discount for existing brands, as indicated by a decrease in the magnitude of own-brand price elasticities. The proposed framework is directly applicable to other fields in which the observed dimension might be change, such as economic, bioinformatics, and so forth. (shrink)

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property (...) on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely. (shrink)

Recently, the research area of mining in structured data has been actively studied. However, since most techniques for structured data mining so far specialize in mining from single structured data, it is difficult for these techniques to handle more realistic data which is related to various types of attribute and which consists of plural kinds of structured data. Since such kind of data is expected to be going to rapidly increase, we need to establish a flexible and highly accurate technique (...) that can inclusively treat such kind of data. In this paper, as one of the techniques to deal with such kind of data, we propose data mining algorithms of mining classification rules in multidimensional structured data. First, an algorithm with two pruning capabilities of mining correlated patterns is introduced. Then, top- k multidimensional correlated patterns are discovered by using this algorithm repeatedly in the fashion like a beam search. We also show the algorithms for constructing classifiers based on the discovered patterns. Experiments with real world data were conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms can construct comprehensible and accurate classifiers within a reasonable running time. (shrink)

Nowadays the increase of traffic causes numerous serious traffic jams, and traffic signals are desired to work adaptively for dynamic traffic flows. In this paper, we view such a problem of traffic signal control as a multi-agent problem where each signal has a controlling agent, and aim to make the agents work cooperatively depending on the traffic status. To build such an agent program automatically, we introduce genetic programming, an evolutionary method for program construction. In GP, it is known as (...) important to encapsulate the substructures of a program which leads to higher fitness to the environment, and we propose a new encapsulation method using an efficient technique for discovering frequent substructures, which has been recently proposed in the data mining field. We also conducted a simulation with a real traffic data, and confirmed that GP with our encapsulation method outperforms the normal GP. It is also observed that the best individual has a communication part that chooses an appropriate communication area and adapts to the traffic status. (shrink)

The purpose of this research is to develop a framework to analyze the content and a process of persuading process and its application to the communication for the debt-collecting process. It is possible for us to understand how the skilled workers have used the keyword groups concerning the motivation to pay, the payment methods and the payment confirmation in their conversations, to model a persuading process. There is no research and method to deal with a large amount of conversation logs (...) for discovering useful knowledge about a persuading process. In this paper, we were successful in discovering a part of the distinctive features of skilled workers in their conversations for the overdue payment collection, applying our method to communication data in a Japanese telecommunications company. (shrink)

We study a regression tree algorithm tailored to casualty insurance pure premium estimation problems. Casualty insurance premium is mainly determined by the expected amount that the insurance companies have to pay for the contract. Therefore, casualy insurance companies have to estimate the expected insurance amount on the basis of insurance risk factors. This problem is formulated as a regression problem, i.e. estimation of conditional mean E[Y|x], where Y is insurance amounts and x is risk factors. In this paper, we aim (...) to implement the regression problem in regression tree framework. The difficulty of the problem lies in the fact that the distribution of insurance amount P is highly skewed and exhibits a long-tail toward positive direction. Conventional least-square-error regression tree algorithm is notoriously unstable under such long-tailed error distribution. On the other hand, several types of robust regression trees, such as least-absolute-error regression tree, are neither appropriate in this situation because they yields significant bias to conditional mean E[Y|x]. In this paper, we propose a two-stage tree fitting algorithm. In the first stage, the algorithm constructs a quantile tree, a kind of robust regression tree, which is stable but biased to conditional mean E[Y|x]. In the second stage, the algorithm corrects the bias using least-square error regression tree. We discuss the theoretical background of the algorithm and empirically investigate the performances. We applied the proposed algorithm to a car insurance data set of 318,564 records provided from a north-american insurance company and obtained significantly better results than conventional regression tree algorithm. (shrink)

We propose a novel probabilistic image selection method for the Web image gathering system we proposed before. It employed two-step processing: (1) Gather HTML files of Web pages related to given keywords, analyze them and fetch only Web images expected to be highly related to the keywords. (2) Select only relevant images from the gathered images based on the image-feature-based clustering. In this paper, we propose building a generative model based on the Gaussian mixture model to represent the distribution of (...) image features of images related to the given keywords, and applying it to select images instead of the processing (2). We call the new system ``Probabilistic Image Collector''. We show the effectiveness of our proposed system by the experimental results. (shrink)

In this paper, we propose the c AS, a new ACO algorithm, and evaluate the performance using TSP instances available at TSPLIB. The results show that c AS works well on the test instances and has performance that may be one of the most promising ACO algorithms. We also evaluate c AS when it is combined with LK local search heuristic using larger sized TSP instances. The results also show promising performance. c AS introduced two important schemes. One is to (...) use the colony model divided into units, which has a stronger exploitation feature while maintaining a certain degree of diversity among units. The other is to use a scheme, we call cunning, when constructing new solutions, which can prevent premature stagnation by reducing strong positive feedback to the trail density. (shrink)

Through numbers of studies on the formation of equivalence relations and causal induction, it is known that human beings tend to consider conditional statements ``if p then q " as biconditional statements ``if and only if p then q ": we call the tendency to perceive ``if p then q " as ``if q then p " the ``symmetry bias". On the other hand, many studies on children's word learning have pointed out that children tend to expect each object has (...) only one label. This is so-called the ``mutual exclusivity bias". This bias implies. (shrink)

Peculiarity oriented mining is different from, and complementary to, existing approaches for discovering new, surprising and interesting patterns hidden in data. A main task of mining peculiarity rules is peculiarity identification. Previous methods for finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining in which peculiar data are identified on the record level. The experimental results in image sequences of tracking multiple walking people show that the proposed RPOM approach is effective.

Genetic Programming is a powerful optimization algorithm, which employs the crossover for genetic operation. Because the crossover operator in GP randomly selects sub-trees, the building blocks may be destroyed by the crossover. Recently, algorithms called PMBGPs based on probabilistic techniques have been proposed in order to improve the problem mentioned above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree, which much reduces a number of possible symbols at each (...) node. Although the large number of symbols gives rise to the large conditional probability table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. Computational experiments on two subjects demonstrate that our new PMBGP is much superior to prior probabilistic models. (shrink)