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Full Description
In 1969, John McCarthy and Pat Hayes uncovered a problem that has haunted the field of artificial intelligence ever since--the frame problem. The problem arises when logic is used to describe the effects of actions and events. Put simply, it is the problem of representing what remains unchanged as a result of an action or event. Many researchers in artificial intelligence believe that its solution is vital to the realization of the field's goals. Solving the Frame Problem presents the various approaches to the frame problem that have been proposed over the years. The author presents the material chronologically--as an unfolding story rather than as a body of theory to be learned by rote. There are lessons to be learned even from the dead ends researchers have pursued, for they deepen our understanding of the issues surrounding the frame problem. In the book's concluding chapters, the author offers his own work on event calculus, which he claims comes very close to a complete solution to the frame problem. Artificial Intelligence series
Contents
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).
