Why is the frame problem? describing the non-effects of actions; introducing the situation calculus; frame axioms; towards a solution; making frame axioms more compact; criteria for a solution to the frame problem; elaboration tolerance; non-monotonic solutions; the common sense law of inertia; monotonic versus non-monotonic solutions; explanations, qualifications and narrative; philosophical reflections. Logical foundations: the language of predicate calculus; the semantics of predicate calculus; many-sorted predicate calculus; second-order predicate calculus; the ontology and language os situation calculus situation calculus formulae; situations and the result function; the limitations of the situation calculus; default reasoning; circumscription; more complicated circumscription policies. Towards a non-monotonic solution: formalising the common sense law of inertia; an example that works; the Hanks-McDermott problem; variations on Hanks and McDermott theme; differences in situation calculus style; the importance of the Hanks-Mcdermott problem. Chronological minisation: the Yale shooting scenario in Default Logic; generating extensions in Default Logic; the directionality of time; formalising chronological minimisation; the Yale shooting scenario; the stolen car scenario; improving chronological minimisation. Casual minimisation: eliminating spontaneous change; the Yale shooting scenarios; the principles of separation and directionality; actions with context-dependent effects; causal minimisation and explanation; ramifications and casual minimisation. Introducing state-based minimisation: varying the result; adding an existence-of-situations axiom; the need for domain closure axioms; a universal existence-of situation axiom. Generalising state-based minimisation: logical prerequisites; first-order formalisations; applying state-based minimisation; state-based minimisation and explanation; a second-order existence-of-situation axiom; general theorems about state-based minimisation. Tailor-made techniques: explanation closure axioms; ramifications and explanation closure; automatically derived frame axioms; successor state axioms; the language A. Narratives in the situation calculus: the need for narratives; arboreality and existence-of-situations; associating a time with each actual situation; two theorems of circumscription; two separation theorems for narratives; associating a situation with each time point; comparing the approaches. (Part contents).
"Shanahan gives a clear exposition of the AI problem in general andlogical AI in particular. He goes on to a clear exposition of theframe problem and many approaches to its solution. Much of this willbecome accepted as authoritative." John McCarthy, Professor of Computer Science, Stanford University "The frame problem is one of the central theoretical issues of artificialintelligence, and considerable progress in the study of this problem hasbeen made over the last years. Shanahan's book provides a clear andcomprehensive treatment of this work. It will be appreciated by everyoneinterested in the logical foundations of artificial intelligence." Vladimir Lifschitz, Gottesman Family Centennial Professorin Computer Sciences, University of Texas at Austin
Murray Shanahan is Professor of Cognitive Robotics in the Department of Computing at Imperial College London. He is the author of Solving the Frame Problem (MIT Press) and Embodiment and the Inner Life.
"Shanahan gives a clear exposition of the AI problem in general andlogical AI in particular. He goes on to a clear exposition of theframe problem and many approaches to its solution. Much of this willbecome accepted as authoritative." John McCarthy, Professor of Computer Science, Stanford University "The frame problem is one of the central theoretical issues of artificialintelligence, and considerable progress in the study of this problem hasbeen made over the last years. Shanahan's book provides a clear andcomprehensive treatment of this work. It will be appreciated by everyoneinterested in the logical foundations of artificial intelligence." Vladimir Lifschitz, Gottesman Family Centennial Professorin Computer Sciences, University of Texas at Austin
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