We discuss a simple logic to describe one of our favourite games from childhood, hide and seek, and show how a simple addition of an equality constant to describe the winning condition of the seeker makes our logic undecidable. There are certain decidable fragments of first-order logic which behave in a similar fashion and we add a new modal variant to that class of logics. We also discuss the relative expressive power of the proposed logic in comparison to the standard (...) modal counterparts. (shrink)

When agents know a protocol, this leads them to have expectations about future observations. Agents can update their knowledge by matching their actual observations with the expected ones. They eliminate states where they do not match. In this paper, we study how agents perceive protocols that are not commonly known, and propose a semantics-driven logical framework to reason about knowledge in such scenarios. In particular, we introduce the notion of epistemic expectation models and a propositional dynamic logic-style epistemic logic for (...) reasoning about knowledge via matching agentsÊ expectations to their observations. It is shown how epistemic expectation models can be obtained from epistemic protocols. Furthermore, a characterization is presented of the effective equivalence of epistemic protocols. We introduce a new logic that incorporates updates of protocols and that can model reasoning about knowledge and observations. Finally, the framework is extended to incorporate fact-changing actions, and a worked-out example is given. © 2013 Elsevier B.V. (shrink)

We make a proposal for formalizing simultaneous games at the abstraction level of player’s powers, combining ideas from dynamic logic of sequential games and concurrent dynamic logic. We prove completeness for a new system of ‘concurrent game logic’ CDGL with respect to finite non-determined games. We also show how this system raises new mathematical issues, and throws light on branching quantifiers and independence-friendly evaluation games for first-order logic.

This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human subjects who (...) produce the outwardly correct ‘backward induction answer’ use a different internal reasoning strategy to achieve it. The paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics help to precisely distinguish different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects. The syntactic framework of the formal system provides a generic way of constructing computational cognitive models of the participants of the Marble Drop game. (shrink)

We discuss a simple logic to describe one of our favourite games from childhood, hide and seek, and show how a simple addition of an equality constant to describe the winning condition of the seeker makes our logic undecidable. There are certain decidable fragments of first-order logic which behave in a similar fashion with respect to such a language extension, and we add a new modal variant to that class. We discuss the relative expressive power of the proposed logic in (...) comparison to the standard modal counterparts. We prove that the model checking problem for the resulting logic is \(\textsf{P}\) -complete. In addition, by exploring the connection with related product logics, we gain more insight towards having a better understanding of the subtleties of the proposed framework. (shrink)

We make a proposal for formalizing simultaneous games at the abstraction level of player's powers, combining ideas from dynamic logic of sequential games and concurrent dynamic logic. We prove completeness for a new system of 'concurrent game logic' CDGL with respect to finite non-determined games. We also show how this system raises new mathematical issues, and throws light on branching quantifiers and independence-friendly evaluation games for first-order logic.

We make a proposal for formalizing simultaneous games at the abstraction level of player’s powers, combining ideas from dynamic logic of sequential games and concurrent dynamic logic. We prove completeness for a new system of ‘concurrent game logic’ CDGL with respect to finite non-determined games. We also show how this system raises new mathematical issues, and throws light on branching quantifiers and independence-friendly evaluation games for first-order logic.

How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in terms of (...) decisions, response times, and answers to questions. We validate these claims on the basis of an earlier game-theoretic experiment about the turn-taking game “Marble Drop with Surprising Opponent”, in which the opponent often starts with a seemingly irrational move. We explore two ways of segregating the participants into reasonable “player types”. The first way is based on latent class analysis, which divides the players into three classes according to their first decisions in the game: Random players, Learners, and Expected players, who make decisions consistent with forward induction. The second way is based on participants’ answers to a question about their opponent, classified according to levels of theory of mind: zero-order, first-order and second-order. It turns out that increasing levels of decisions and theory of mind both correspond to increasing success as measured by monetary awards and increasing decision times. Next, we use the logical language to express different kinds of strategies that people apply when reasoning about their opponent and making decisions in turn-taking games, as well as the ‘reasoning types’ reflected in their behavior. Then, we translate the logical formulas into computational cognitive models in the PRIMs architecture. Finally, we run two of the resulting models, corresponding to the strategy of only being interested in one’s own payoff and to the myopic strategy, in which one can only look ahead to a limited number of nodes. It turns out that the participant data fit to the own-payoff strategy, not the myopic one. The article closes the circle from experiments via logic and cognitive modelling back to predictions about new experiments. (shrink)

There are two important aspects of any democratic decision: aggregation of preferences and deliberation about preferences. They are essential and complementary components of any decision making process. While the well-studied process of aggregation focuses on accumulating individual preferences without discussing their origin [4], deliberation can be seen as a conversation through which individuals justify their preferences, a process that might lead to changes in their opinions as they get influenced by one another. Till now, there has been a lot of (...) work on the ‘aggregation’ aspect (e.g., [12, 14, 6]). However, some recent work has focussed on the deliberation aspect as well [8, 9, 10, 15]. Sometimes, deliberation does not lead to unanimity in preferences, but the discussion can lead to some ‘preference uniformity’ (see how deliberation can help in bypassing social choice theory’s impossibility results in [5]), which might facilitate their eventual aggregation. In addition, the combination of both processes provides a more realistic model for decision making scenarios. In light of this status quo, our focus is on the formal study of achieving such preference uniformities, e.g., single-peaked, single-caved, single-crossing, value-restricted, best-restricted, worst-restricted, medium-restricted, or group-separable profiles. In this short abstract we provide our preliminary ideas towards achieving singlepeakedness of preference profiles via deliberation. In what follows, we define two preference upgrade operators based on [8, 9] and provide a preliminary discussion on how single-peaked preference profiles can be achieved through such operations. We will delve into the details of the logical language in the main paper. (shrink)

This LNCS book is part of the FOLLI book series and constitutes the proceedings of the 8th International Workshop on Logic, Rationality, and Interaction, LORI 2021, held in Xi`an, China, in October 2021. The 15 full papers presented together with 7 short papers in this book were carefully reviewed and selected from 40 submissions. The workshop covers a wide range on the following topics such as doxastic and epistemic logics, deontic logic, intuitionistic and subsstructural logics, voting theory, and causal inference.

Study of Saṅkṣepaśārīraka of Sarvajñātman, condensed version of Śārīrakamīmāṃsābhāṣya of Śaṅkarācārya, work on Advaita Vedanta philosophy.

Product Dimensions: 8.6 x 5.6 x 1.1 inches Description: Very little is known about the author of this book apart from the facts that he is a retired Government of India officer, now in his late nineties, apparently hoary, but healthy. When requested for more bio-data, he wrote back…The Real author of the Original Yoga is the Lord Siva. In the mundane world, Patanjali is the prime propagator of yoga. Any other claim to authorship, therefore, cannot but be spurious. …It (...) is Truth that matters, not the utterer of the Truth. No Truth ever becomes a bit truer even when repeated by the most distinguished person. …Hence all genuine aspirants of yoga should be content to remain unknown and unnoticed by others. His other published work is Hindu Concept of Life and Death. (shrink)

Correspondence between Aurobindo Ghose, 1872-1950 and Dilip Kumar Roy, 1897-1980.

On the philosophical concepts of the religion of the Khasis, ethnic people from Meghalaya.

Since the conceptualization of unethical pro-organizational behavior ten years ago, scholarly interest in exploring this phenomenon has multiplied. Given a burgeoning body of empirical research, a review of unethical pro-organizational behavior literature is warranted. This study, therefore, systematically reviews the extant literature on unethical pro-organizational behavior and presents a comprehensive theory-based review of the past developments in this field. We classify previous studies based on their underlying theoretical perspectives and discuss the antecedents and consequences of unethical pro-organizational behavior in work (...) context. We also explicate the boundary conditions under which the influence of these antecedents gets accentuated or alleviated. Overall, this study synthesizes past knowledge to elucidate why, how, and when unethical pro-organizational behavior unfolds in the workplace. Finally, the gaps in the extant theorization are identified and an agenda for future research is proposed. (shrink)

The welfare and protection of human subjects is critical to the integrity of clinical investigation and research. Institutional review boards were thus set up to be impartial reviewers of research protocols in clinical research. Their main role is to stand between the investigator and her human subjects in order to ensure that the welfare of human subjects are protected. While there is much literature on the conflicts of interest faced by investigators and researchers in clinical investigations, an area that is (...) less explored is CIs that may affect members of IRBs during the institutional ethics review of clinical investigations. This article examines the notion of CIs in clinical research and attempts to develop a framework for a clearer and more balanced approach to identifying CIs that may influence members of IRBs and impede their independence. It will also apply the proposed framework to demonstrate how IRBs possess, or at least may appear to possess, forms of financial CIs and non-financial CIs. The proper identification and management of these CIs is critical to preserving the integrity of clinical investigations and achieving the primary aim of human subjects protection. (shrink)

In the domain of legal information retrieval, an important challenge is to compute similarity between two legal documents. Precedents play an important role in The Common Law system, where lawyers need to frequently refer to relevant prior cases. Measuring document similarity is one of the most crucial aspects of any document retrieval system which decides the speed, scalability and accuracy of the system. Text-based and network-based methods for computing similarity among case reports have already been proposed in prior works but (...) not without a few pitfalls. Since legal citation networks are generally highly disconnected, network based metrics are not suited for them. Till date, only a few text-based and predominant embedding based methods have been employed, for instance, TF-IDF based approaches, Word2Vec and Doc2Vec based approaches. We investigate the performance of 56 different methodologies for computing textual similarity across court case statements when applied on a dataset of Indian Supreme Court Cases. Among the 56 different methods, thirty are adaptations of existing methods and twenty-six are our proposed methods. The methods studied include models such as BERT and Law2Vec. It is observed that the more traditional methods that rely on a bag-of-words representation performs better than the more advanced context-aware methods for computing document-level similarity. Finally we nominate, via empirical validation, five of our best performing methods as appropriate for measuring similarity between case reports. Among these five, two are adaptations of existing methods and the other three are our proposed methods. (shrink)

In a Common Law system, legal practitioners need frequent access to prior case documents that discuss relevant legal issues. Case documents are generally very lengthy, containing complex sentence structures, and reading them fully is a strenuous task even for legal practitioners. Having a concise overview of these documents can relieve legal practitioners from the task of reading the complete case statements. Legal catchphrases are (multi-word) phrases that provide a concise overview of the contents of a case document, and automated generation (...) of catchphrases is a challenging problem in legal analytics. In this paper, we propose a novel supervised neural sequence tagging model for the extraction of catchphrases from legal case documents. Specifically, we show that incorporating document-specific information along with a sequence tagging model can enhance the performance of catchphrase extraction. We perform experiments over a set of Indian Supreme Court case documents, for which the gold-standard catchphrases (annotated by legal practitioners) are obtained from a popular legal information system. The performance of our proposed method is compared with that of several existing supervised and unsupervised methods, and our proposed method is empirically shown to be superior to all baselines. (shrink)

The task of rhetorical role labeling is to assign labels (such as Fact, Argument, Final Judgement, etc.) to sentences of a court case document. Rhetorical role labeling is an important problem in the field of Legal Analytics, since it can aid in various downstream tasks as well as enhances the readability of lengthy case documents. The task is challenging as case documents are highly various in structure and the rhetorical labels are often subjective. Previous works for automatic rhetorical role identification (...) (i) mainly used Conditional Random Fields over manually handcrafted features, and (ii) focused on certain law domains only (e.g., Immigration cases, Rent law), and a particular jurisdiction/country (e.g., US, Canada, India). In this work, we improve upon the prior works on rhetorical role identification by proposing novel Deep Learning models for automatically identifying rhetorical roles, which substantially outperform the prior methods. Additionally, we show the effectiveness of the proposed models over documents from five different law domains, and from two different jurisdictions—the Supreme Court of India and the Supreme Court of the UK. Through extensive experiments over different variations of the Deep Learning models, including Transformer models based on BERT and LegalBERT, we show the robustness of the methods for the task. We also perform an extensive inter-annotator study and analyse the agreement of the predictions of the proposed model with the annotations by domain experts. We find that some rhetorical labels are inherently hard/subjective and both law experts and neural models frequently get confused in predicting them correctly. (shrink)

The Transforming growth factor beta (TGF‐β) family of secreted proteins regulates a variety of key events in normal development and physiology. In mammals, this family, represented by 33 ligands, including TGF‐β, activins, nodal, bone morphogenetic proteins (BMPs), and growth and differentiation factors (GDFs), regulate biological processes as diverse as cell proliferation, differentiation, apoptosis, metabolism, homeostasis, immune response, wound repair, and endocrine functions. In Drosophila, only 7 members of this family are present, with 4 TGF‐β/BMP and 3 TGF‐β/activin ligands. Studies in (...) the fly have illustrated the role of TGF‐β/BMP ligands during embryogenesis and organ patterning, while the TGF‐β/activin ligands have been implicated in the control of wing growth and neuronal functions. In this review, we focus on the emerging roles of Drosophila TGF‐β/activins in inter‐organ communication via long‐distance regulation, especially in systemic lipid and carbohydrate homeostasis, and discuss findings relevant to metabolic diseases in humans. -/- . (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase (...) in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Summarization of legal case judgement documents is a practical and challenging problem, for which many summarization algorithms of different varieties have been tried. In this work, rather than developing yet another summarization algorithm, we investigate if intelligently ensembling (combining) the outputs of multiple (base) summarization algorithms can lead to better summaries of legal case judgements than any of the base algorithms. Using two datasets of case judgement documents from the Indian Supreme Court, one with extractive gold standard summaries and the (...) other with abstractive gold standard summaries, we apply various ensembling techniques on summaries generated by a wide variety of summarization algorithms. The ensembling methods applied range from simple voting-based methods to ranking-based and graph-based ensembling methods. We show that many of our ensembling methods yield summaries that are better than the summaries produced by any of the individual base algorithms, in terms of ROUGE and METEOR scores. (shrink)

Student support services are the cardinal features of the Open and Distance Learning System. In India, the Indira Gandhi National Open University, 13 state open universities and over 200 Distance Education Institutes attached to conventional universities and private/autonomous institutes offering programmes through the distance mode have all created adequate provisions for learner support services and the systems, rules, regulations and norms have been put in place. However, it is significant that a learner in the ODL system remains away from the (...) teacher and the peer group. Thus, the learner feels isolated and needs the empathy of all the functionaries concerned. It has been our experience at IGNOU that more often than not the functionaries fail to realize the value of a learner and the learner fails to understand the value attributed to him by the ODL system. Without such realization the rules and regulations lose their significance. Hence, inculcation of values is of paramount importance in the functioning of the ODL system. In this article we have discussed about ‘inculcation of values’ among the ‘learners’ as well as the ‘functionaries’ for developing ‘best practices’ in learner support services in the ODL system. The inputs are based primarily on direct experiences of the authors. (shrink)

India’s energy demand is predicted to rise by 135% within a span of 20 years. Coping up with surging energy demands requires several reforms in both renewable and non-renewable sectors. Factors such as rising population, reduction in the cost of renewable energy technology and their effect on the nation’s GDP, can make policy making a herculean task and the justification for such policies, quite opaque to the public. Artificial Intelligence technology can help decision makers to quickly draw conclusions from voluminous (...) datasets under different heads. However, AI results need to be post-processed so that they are easily understood by the layperson. This paper focuses on using Sankey diagrams as a post-processing tool for AI systems. Policy formulation often requires an overall assessment of energy sources, production pathways, end used destinations and wastage encountered—these can be easily visualized with a Sankey diagram. For this work, a Sankey protocol was written out in a form where a prompt asks the user to supply information through a spreadsheet interfaced with a customized mobile app—a first anywhere in the subcontinent. India’s mobile sector is vibrant, and the paper presents a enabling the user to have a handle on the dynamics of energy distribution locally. The app gives an instant feel of the apportioned energy across grids, the projected changes for the next decade, the efficiency and feasibility of each sector and lastly a telling visual representation showing the main strengths and weaknesses in the energy sector. (shrink)