Principles of Artificial IntelligenceA classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study. |
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Page 333
... DCOMP We call our next system for dealing with interacting goals DCOMP . It operates in two main phases . In phase 1 , DCOMP produces a tentative " solution , " assuming that there are no goal interactions . Goal expressions are ...
... DCOMP We call our next system for dealing with interacting goals DCOMP . It operates in two main phases . In phase 1 , DCOMP produces a tentative " solution , " assuming that there are no goal interactions . Goal expressions are ...
Page 349
... DCOMP is led to continue the search for a solution by establishing the precondition , CONT ( rl , B ) , of F - rule 2 but now prohibiting the substitution { Y / rl } . Continued search results in the tentative solution shown in Figure ...
... DCOMP is led to continue the search for a solution by establishing the precondition , CONT ( rl , B ) , of F - rule 2 but now prohibiting the substitution { Y / rl } . Continued search results in the tentative solution shown in Figure ...
Page 359
... DCOMP would achieve the goal [ ON ( A , B ) ^ ON ( B , C ) ] . 8.5 Sketch out the design of a hierarchical version of DCOMP that bears the same relationship to DCOMP that ABSTRIPS bears to STRIPS . ( We might call the system AB - DCOMP ...
... DCOMP would achieve the goal [ ON ( A , B ) ^ ON ( B , C ) ] . 8.5 Sketch out the design of a hierarchical version of DCOMP that bears the same relationship to DCOMP that ABSTRIPS bears to STRIPS . ( We might call the system AB - DCOMP ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
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8-puzzle achieve actions Adders AI production algorithm AND/OR graph applied Artificial Intelligence atomic formula backed-up value backtracking backward block breadth-first breadth-first search called chapter clause form CLEAR(C component contains control regime control strategy cost DCOMP Deleters delineation depth-first search described discussed disjunction domain element-of evaluation function example existentially quantified F-rule formula frame problem global database goal expression goal node goal stack goal wff graph-search HANDEMPTY heuristic HOLDING(B implication initial state description knowledge leaf nodes literal nodes logic negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production system proof prove recursive regress represent representation result robot problem rule applications search graph search tree selected semantic network sequence shown in Figure Skolem function solution graph solve SRI International stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes unifying composition universally quantified unstack(C,A variables WORKS-IN