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Last updated in SearchWorks on December 3, 2023 5:50pm
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a| 1. Planning and autonomous behavior -- 1.1 Autonomous behavior: hardwired, learned, and model-based -- 1.2 Planning models and languages -- 1.3 Generality, complexity, and scalability -- 1.4 Examples -- 1.5 Generalized planning: plans vs. general strategies -- 1.6 History.
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a| 2. Classical planning: full information and deterministic actions -- 2.1 Classical planning model -- 2.2 Classical planning as path finding -- 2.3 Search algorithms: blind and heuristic -- 2.4 Online search: thinking and acting interleaved -- 2.5 Where do heuristics come from? -- 2.6 Languages for classical planning -- 2.7 Domain-independent heuristics and relaxations -- 2.8 Heuristic search planning -- 2.9 Decomposition and goal serialization -- 2.10 Structure, width, and complexity.
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a| 3. Classical planning: variations and extensions -- 3.1 Relaxed plans and helpful actions -- 3.2 Multi-queue best-first search -- 3.3 Implicit subgoals: landmarks -- 3.4 State-of-the-art classical planners -- 3.5 Optimal planning and admissible heuristics -- 3.6 Branching schemes and problem spaces -- 3.7 Regression planning -- 3.8 Planning as SAT and constraint satisfaction -- 3.9 Partial-order causal link planning -- 3.10 Cost, metric, and temporal planning -- 3.11 Hierarchical task networks.
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a| 7. POMDP planning: stochastic actions and partial feedback -- 7.1 Goal, shortest-path, and discounted POMDPs -- 7.2 Exact offline algorithms -- 7.3 Approximate and online algorithms -- 7.4 Belief tracking in POMDPs -- 7.5 Other MDP and POMDP solution methods.
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a| Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
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