Cover Image

A concise introduction to models and methods for automated planning /

Bibliographic Details
Main Author: Geffner, Hector
Other Authors: Bonet, Blai (Computer scientist)
Format: eBook
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, [2013]
Series:Synthesis lectures on artificial intelligence and machine learning ; #22.
Subjects:
Artificial intelligence.
Planning > Automation.
Planning > Computer simulation.
COMPUTERS / Enterprise Applications / Business Intelligence Tools.
COMPUTERS / Intelligence (AI) & Semantics.
MDP and POMDP planning
action selection
autonomous behavior
belief tracking
domain-independent problem solving
model-based control
plan generation and recognition
planning
planning with incomplete information and sensing
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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
  • 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
  • 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 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
  • 8. Discussion
  • 8.1 Challenges and open problems
  • 8.2 Planning, scalability, and cognition
  • Bibliography
  • Author's biography.