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  1. A Computational Learning Semantics for Inductive Empirical Knowledge.Kevin T. Kelly - 2014 - In Alexandru Baltag & Sonja Smets (eds.), Johan van Benthem on Logic and Information Dynamics. Cham, Switzerland: Springer International Publishing. pp. 289-337.
    This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...)
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  • A Tractable and Expressive Class of Marginal Contribution Nets and Its Applications.Edith Elkind, Leslie Ann Goldberg, Paul W. Goldberg & Michael Wooldridge - 2009 - Mathematical Logic Quarterly 55 (4):362-376.
    Coalitional games raise a number of important questions from the point of view of computer science, key among them being how to represent such games compactly, and how to efficiently compute solution concepts assuming such representations. Marginal contribution nets , introduced by Ieong and Shoham, are one of the simplest and most influential representation schemes for coalitional games. MC-nets are a rulebased formalism, in which rules take the form pattern → value, where “pattern ” is a Boolean condition over agents, (...)
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  • What will they say?—Public Announcement Games.Hans van Ditmarsch & Thomas Ågotnes - 2011 - Synthese 179 (S1):57 - 85.
    Dynamic epistemic logic describes the possible information-changing actions available to individual agents, and their knowledge pre-and post conditions. For example, public announcement logic describes actions in the form of public, truthful announcements. However, little research so far has considered describing and analysing rational choice between such actions, i.e., predicting what rational self-interested agents actually will or should do. Since the outcome of information exchange ultimately depends on the actions chosen by all the agents in the system, and assuming that agents (...)
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  • Discrete preference games with logic-based agents: Formal framework, complexity, and islands of tractability.Gianluigi Greco & Marco Manna - 2024 - Artificial Intelligence 332 (C):104131.
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  • Compactly representing utility functions using weighted goals and the max aggregator.Joel Uckelman & Ulle Endriss - 2010 - Artificial Intelligence 174 (15):1222-1246.
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  • Representing Concepts by Weighted Formulas.Daniele Porello & Claudio Masolo - 2018 - In Stefano Borgo, Pascal Hitzler & Oliver Kutz (eds.), Formal Ontology in Information Systems - Proceedings of the 10th International Conference, {FOIS} 2018, Cape Town, South Africa, 19-21 September 2018. IOS Press. pp. 55--68.
    A concept is traditionally defined via the necessary and sufficient conditions that clearly determine its extension. By contrast, cognitive views of concepts intend to account for empirical data that show that categorisation under a concept presents typicality effects and a certain degree of indeterminacy. We propose a formal language to compactly represent concepts by leveraging on weighted logical formulas. In this way, we can model the possible synergies among the qualities that are relevant for categorising an object under a concept. (...)
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  • Johan van Benthem on Logic and Information Dynamics.Alexandru Baltag & Sonja Smets (eds.) - 2014 - Cham, Switzerland: Springer International Publishing.
    This book illustrates the program of Logical-Informational Dynamics. Rational agents exploit the information available in the world in delicate ways, adopt a wide range of epistemic attitudes, and in that process, constantly change the world itself. Logical-Informational Dynamics is about logical systems putting such activities at center stage, focusing on the events by which we acquire information and change attitudes. Its contributions show many current logics of information and change at work, often in multi-agent settings where social behavior is essential, (...)
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  • Relational preference rules for control.Ronen I. Brafman - 2011 - Artificial Intelligence 175 (7-8):1180-1193.
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  • Voting almost maximizes social welfare despite limited communication.Ioannis Caragiannis & Ariel D. Procaccia - 2011 - Artificial Intelligence 175 (9-10):1655-1671.
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