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  1. A Formalism to Specify Unambiguous Instructions Inspired by Mīmāṁsā in Computational Settings.Bama Srinivasan & Ranjani Parthasarathi - 2022 - Logica Universalis 16 (1):27-55.
    Mīmāṁsā, an Indian hermeneutics provides an exhaustive methodology to interpret Vedic statements. A formalism namely, Mīmāṁsā Inspired Representation of Actions has already been proposed in a preliminary manner. This paper expands the formalism logically and includes Syntax and Semantics covering Soundness and Completeness. Here, several interpretation techniques from Mīmāṁsā have been considered for formalising the statements. Based on these, instructions that denote actions are categorized into positive and prohibitive unconditional imperatives and conditional imperatives that enjoin reason, temporal action and goal. (...)
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  • Too Many Cooks: Bayesian Inference for Coordinating Multi‐Agent Collaboration.Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes & Max Kleiman-Weiner - 2021 - Topics in Cognitive Science 13 (2):414-432.
    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. (...)
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  • Learning action models with minimal observability.Diego Aineto, Sergio Jiménez Celorrio & Eva Onaindia - 2019 - Artificial Intelligence 275 (C):104-137.
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  • Autonomous agents modelling other agents: A comprehensive survey and open problems.Stefano V. Albrecht & Peter Stone - 2018 - Artificial Intelligence 258 (C):66-95.
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  • HTN planning: Overview, comparison, and beyond.Ilche Georgievski & Marco Aiello - 2015 - Artificial Intelligence 222:124-156.
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  • From systems to logic in the early development of nonmonotonic reasoning.Erik Sandewall - 2011 - Artificial Intelligence 175 (1):416-427.
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  • Reasoning agents in a dynamic world: The frame problem.Jozsef A. Toth - 1995 - Artificial Intelligence 73 (1-2):323-369.
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  • Artificial intelligence and automatic programming in CAI.Elliot B. Koffman & Sumner-E. Blount - 1975 - Artificial Intelligence 6 (3):215-234.
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  • Non-monotonic logic I.Drew McDermott & Jon Doyle - 1980 - Artificial Intelligence 13 (1-2):41-72.
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  • Towards a general theory of action and time.James F. Allen - 1984 - Artificial Intelligence 23 (2):123-154.
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  • Design by derivational analogy:Issues in the automated replay of design plans.Jack Mostow - 1989 - Artificial Intelligence 40 (1-3):119-184.
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  • Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition.Rina Dechter - 1990 - Artificial Intelligence 41 (3):273-312.
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  • O-Plan: The open planning architecture.Ken Currie & Austin Tate - 1991 - Artificial Intelligence 52 (1):49-86.
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  • Intelligent control.Barbara Hayes-Roth - 1993 - Artificial Intelligence 59 (1-2):213-220.
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  • The uses of plans.Martha E. Pollack - 1992 - Artificial Intelligence 57 (1):43-68.
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  • The computational complexity of propositional STRIPS planning.Tom Bylander - 1994 - Artificial Intelligence 69 (1-2):165-204.
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  • Reaching agreements through argumentation: a logical model and implementation.Sarit Kraus, Katia Sycara & Amir Evenchik - 1998 - Artificial Intelligence 104 (1-2):1-69.
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  • A general programming language for unified planning and control.Richard Levinson - 1995 - Artificial Intelligence 76 (1-2):319-375.
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  • Expressive equivalence of planning formalisms.Christer Bäckström - 1995 - Artificial Intelligence 76 (1-2):17-34.
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  • Complexity, decidability and undecidability results for domain-independent planning.Kutluhan Erol, Dana S. Nau & V. S. Subrahmanian - 1995 - Artificial Intelligence 76 (1-2):75-88.
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  • Computational research on interaction and agency.Philip E. Agre - 1995 - Artificial Intelligence 72 (1-2):1-52.
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  • Goal-directed diagnosis—a diagnostic reasoning framework for exploratory-corrective domains.Ron Rymon - 1996 - Artificial Intelligence 84 (1-2):257-297.
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  • Planning as heuristic search.Blai Bonet & Héctor Geffner - 2001 - Artificial Intelligence 129 (1-2):5-33.
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  • Classical Computational Models.Richard Samuels - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge. pp. 103-119.
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  • Manipulating Games by Sharing Information.John Grant, Sarit Kraus, Michael Wooldridge & Inon Zuckerman - 2014 - Studia Logica 102 (2):267-295.
    We address the issue of manipulating games through communication. In the specific setting we consider (a variation of Boolean games), we assume there is some set of environment variables, the values of which are not directly accessible to players; the players have their own beliefs about these variables, and make decisions about what actions to perform based on these beliefs. The communication we consider takes the form of (truthful) announcements about the values of some environment variables; the effect of an (...)
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  • What's wrong with story grammars.Alan Garnham - 1983 - Cognition 15 (1-3):145-154.
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  • The cognitive map must be a separate module.Benjamin Kuipers - 1982 - Behavioral and Brain Sciences 5 (4):645-646.
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  • A maze in graphs.Christopher K. Riesbeck - 1982 - Behavioral and Brain Sciences 5 (4):648-648.
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  • Maps, space, and places.Roger M. Downs - 1982 - Behavioral and Brain Sciences 5 (4):641-642.
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  • Human spatial learning.Kristina Hooper - 1982 - Behavioral and Brain Sciences 5 (4):642-643.
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  • Elements of a Plan‐Based Theory of Speech Acts.Philip R. Cohen & C. Raymond Perrault - 1979 - Cognitive Science 3 (3):177-212.
    This paper explores the truism that people think about what they say. It proposes that, to satisfy their own goals, people often plan their speech acts to affect their listeners' beliefs, goals, and emotional states. Such language use can be modelled by viewing speech acts as operators in a planning system, thus allowing both physical and speech acts to be integrated into plans. Methodological issues of how speech acts should be defined in a planbased theory are illustrated by defining operators (...)
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  • A Commitment‐Based Framework for Describing Informal Cooperative Work.Richard E. Fikes - 1982 - Cognitive Science 6 (4):331-347.
    In this paper we present a framework for describing cooperative work in informal domains such as an office. We argue that standard models of such work are inadequate for describing the adaptibility and variability observed in offices, and are fundamentally misleading as metaphors for understanding the skills and knowledge needed by computers or people to do the work. The basic claim in our alternative framework is that an agent's work is defined in terms of making and fulfilling commitments to other (...)
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  • Meta‐Planning: Representing and Using Knowledge About Planning in Problem Solving and Natural Language Understanding.Robert Wilensky - 1981 - Cognitive Science 5 (3):197-233.
    This paper is concerned with those elements of planning knowledge that are common to both understanding someone else's plan and creating a plan for one's own use. This planning knowledge can be divided into two bodies: Knowledge about the world, and knowledge about the planning process itself. Our interest here is primarily with the latter corpus. The central thesis is that much of the knowledge about the planning process itself can be formulated in terms of higher‐level goals and plans called (...)
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  • Classical computationalism and the many problems of cognitive relevance.Richard Samuels - 2010 - Studies in History and Philosophy of Science Part A 41 (3):280-293.
    In this paper I defend the classical computational account of reasoning against a range of highly influential objections, sometimes called relevance problems. Such problems are closely associated with the frame problem in artificial intelligence and, to a first approximation, concern the issue of how humans are able to determine which of a range of representations are relevant to the performance of a given cognitive task. Though many critics maintain that the nature and existence of such problems provide grounds for rejecting (...)
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  • The Situation Calculus: A Case for Modal Logic. [REVIEW]Gerhard Lakemeyer - 2010 - Journal of Logic, Language and Information 19 (4):431-450.
    The situation calculus is one of the most established formalisms for reasoning about action and change. In this paper we will review the basics of Reiter’s version of the situation calculus, show how knowledge and time have been addressed in this framework, and point to some of the weaknesses of the situation calculus with respect to time. We then present a modal version of the situation calculus where these problems can be overcome with relative ease and without sacrificing the advantages (...)
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  • Sentence generation as a planning problem.Alexander Koller & Matthew Stone - unknown
    We translate sentence generation from TAG grammars with semantic and pragmatic information into a planning problem by encoding the contribution of each word declaratively and explicitly. This allows us to exploit the performance of off-the-shelf planners. It also opens up new perspectives on referring expression generation and the relationship between language and action.
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  • The semantics of variables in action descriptions.Vladimir Lifschitz & W. Ren - manuscript
    structures, or interpretations, in the sense of first-order logic. In C+, on the other hand, a state is an interpreta-.
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  • Some considerations on branching areas of time.ElŻbieta Hajnicz - 1999 - Journal of Logic, Language and Information 8 (1):17-43.
    In this paper we show that properties of non-linear time structures have not been studied enough. Axioms forcing the existence of a branching point in a branching area of a structure are presented for various classes of structures. We show also that the classical Dedekind continuity axiom does not work well in non-linear structures and we suggest stronger versions. Finally, some interdependencies between the axioms presented are proved.
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  • Search and Reasoning in problem solving.Herbert A. Simon - 1983 - Artificial Intelligence 21 (1-2):7-29.
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  • Representing the Zoo World and the Traffic World in the language of the causal calculator.Varol Akman, Selim T. Erdoğan, Joohyung Lee, Vladimir Lifschitz & Hudson Turner - 2004 - Artificial Intelligence 153 (1-2):105-140.
    The work described in this report is motivated by the desire to test the expressive possibilities of action language C+. The Causal Calculator (CCalc) is a system that answers queries about action domains described in a fragment of that language. The Zoo World and the Traffic World have been proposed by Erik Sandewall in his Logic Modelling Workshop—an environment for communicating axiomatizations of action domains of nontrivial size. -/- The Zoo World consists of several cages and the exterior, gates between (...)
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  • A logical theory of robot problem solving.Olga Štěpánková & Ivan M. Havel - 1976 - Artificial Intelligence 7 (2):129-161.
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  • Geometric reasoning and artificial intelligence: Introduction to the special volume.Deepak Kapur & Joseph L. Mundy - 1988 - Artificial Intelligence 37 (1-3):1-11.
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  • Remote Agent: to boldly go where no AI system has gone before.Nicola Muscettola, P. Pandurang Nayak, Barney Pell & Brian C. Williams - 1998 - Artificial Intelligence 103 (1-2):5-47.
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  • (1 other version)Speeding up problem solving by abstraction: a graph oriented approach.R. C. Holte, T. Mkadmi, R. M. Zimmer & A. J. MacDonald - 1996 - Artificial Intelligence 85 (1-2):321-361.
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  • Knowledge-based proof planning.Erica Melis & Jörg Siekmann - 1999 - Artificial Intelligence 115 (1):65-105.
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  • (1 other version)The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
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  • Linear temporal logic as an executable semantics for planning languages.Marta Cialdea Mayer, Carla Limongelli, Andrea Orlandini & Valentina Poggioni - 2006 - Journal of Logic, Language and Information 16 (1):63-89.
    This paper presents an approach to artificial intelligence planning based on linear temporal logic (LTL). A simple and easy-to-use planning language is described, Planning Domain Description Language with control Knowledge (PDDL-K), which allows one to specify a planning problem together with heuristic information that can be of help for both pruning the search space and finding better quality plans. The semantics of the language is given in terms of a translation into a set of LTL formulae. Planning is then reduced (...)
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  • Applicability conditions for plans with loops: Computability results and algorithms.Siddharth Srivastava, Neil Immerman & Shlomo Zilberstein - 2012 - Artificial Intelligence 191-192 (C):1-19.
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  • Reasoning from last conflict(s) in constraint programming.Christophe Lecoutre, Lakhdar Saïs, Sébastien Tabary & Vincent Vidal - 2009 - Artificial Intelligence 173 (18):1592-1614.
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  • Learning and executing generalized robot plans.Richard E. Fikes, Peter E. Hart & Nils J. Nilsson - 1972 - Artificial Intelligence 3 (C):251-288.
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