| 000 | 06074nam a2200745 i 4500 | ||
|---|---|---|---|
| 001 | 6812804 | ||
| 003 | IEEE | ||
| 005 | 20200413152910.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 130718s2013 caua foab 000 0 eng d | ||
| 020 | _a9781608459704 (electronic bk.) | ||
| 020 | _z9781608459698 (pbk.) | ||
| 024 | 7 |
_a10.2200/S00513ED1V01Y201306AIM022 _2doi |
|
| 035 | _a(CaBNVSL)swl00402609 | ||
| 035 | _a(OCoLC)853278184 | ||
| 040 |
_aCaBNVSL _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQ335 _b.G443 2013 |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 090 |
_a _bMoCl _e201306AIM022 |
||
| 100 | 1 | _aGeffner, Hector. | |
| 245 | 1 | 2 |
_aA concise introduction to models and methods for automated planning _h[electronic resource] / _cHector Geffner, Blai Bonet. |
| 260 |
_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _cc2013. |
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| 300 |
_a1 electronic text (xii, 129 p.) : _bill., digital file. |
||
| 490 | 1 |
_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 22 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (p. 113-127). | ||
| 505 | 0 | _a1. 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 -- | |
| 505 | 8 | _a2. 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 -- | |
| 505 | 8 | _a3. 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 -- | |
| 505 | 8 | _a4. Beyond classical planning: transformations -- 4.1 Soft goals and rewards -- 4.2 Incomplete information -- 4.3 Plan and goal recognition -- 4.4 Finite-state controllers -- 4.5 Temporally extended goals -- | |
| 505 | 8 | _a5. Planning with sensing: logical models -- 5.1 Model and language -- 5.2 Solutions and solution forms -- 5.3 Offline solution methods -- 5.4 Online solution methods -- 5.5 Belief tracking: width and complexity -- 5.6 Strong vs. strong cyclic solutions -- | |
| 505 | 8 | _a6. MDP planning: stochastic actions and full feedback -- 6.1 Goal, shortest-path, and discounted models -- 6.2 Dynamic programming algorithms -- 6.3 Heuristic search algorithms -- 6.4 Online MDP planning -- 6.5 Reinforcement learning, model-based RL, and planning -- | |
| 505 | 8 | _a7. 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 -- | |
| 505 | 8 | _a8. Discussion -- 8.1 Challenges and open problems -- 8.2 Planning, scalability, and cognition -- | |
| 505 | 8 | _aBibliography -- Author's biography. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aPlanning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF t.p. (viewed on July 18, 2013). | ||
| 650 | 0 |
_aArtificial intelligence _xPlanning. |
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| 650 | 0 |
_aPlanning _xComputer simulation. |
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| 653 | _aplanning | ||
| 653 | _aautonomous behavior | ||
| 653 | _amodel-based control | ||
| 653 | _aplan generation and recognition | ||
| 653 | _aMDP and POMDP planning | ||
| 653 | _aplanning with incomplete information and sensing | ||
| 653 | _aaction selection | ||
| 653 | _abelief tracking | ||
| 653 | _adomain-independent problem solving | ||
| 700 | 1 | _aBonet, Blai. | |
| 776 | 0 | 8 |
_iPrint version: _z9781608459698 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on artificial intelligence and machine learning ; _v# 22. _x1939-4616 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812804 |
| 856 | 4 | 0 |
_3Abstract with links to full text _uhttp://dx.doi.org/10.2200/S00513ED1V01Y201306AIM022 |
| 999 |
_c562001 _d562001 |
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