Planning and control in artificial intelligence: A unifying perspective

Applied Intelligence

Volumn 14, Issue 3, 2001, Pages 237-252

  Bonet, Blai ^a   Geffner, Héctor ^a  

^a UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

Author keywords

Markov Decision Processes; Planning with incomplete information; Planning with uncertainty

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; FEEDBACK CONTROL; HEURISTIC PROGRAMMING; HIGH LEVEL LANGUAGES; LOGIC PROGRAMMING; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING;

HEURISTIC SEARCH ALGORITHMS; HIGH LEVEL REPRESENTATIONS LANGUAGES;

ARTIFICIAL INTELLIGENCE;

EID: 0035330792     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1011286518035     Document Type: Article

References (59)

1. A robust layered control system for a mobile robot
2. What are plans for?
3. STRIPS: A new approach to the application of theorem proving to problem solving
4. ADL: Exploring the middle ground between strips and the situation calcules
5. Representing action and change by logic programs
6. Modeling action, knowledge and control
7. Learning to act using real-time dynamic programming
8. A robust and fast action selection mechanism for planning
9. Solving large POMDPs using real time dynamic programming
10. Heuristic search in cyclic AND/OR graphs
11. Planning under uncertainty: Structural assumptions and computational leverage
12. Planning and acting in partially observable stochastic domains
13. Planning as heuristic search: New results
14. Planning with deadlines in stochastic domains
15. Universal plans for reactive robots in unpredictable environments
16. Teleo-reactive programs for agent control
17. Acting optimally in partially observable stochastic domains
18. Optimal control of markov decision processes with incomplete state estimation
19. What is planning in the presence of sensing
20. Planning under uncertainty: Some key issues
21. Extending Graphplan to handle uncertainty and sensing actions
22. Consideration of risk in reinforcement learning
23. Real-time search in non-deterministic domains
24. Conformant graphplan
25. Conformant planning via model checking
26. Real-time heuristic search
27. Solving pomdps by searching in policy space
28. BI-POMDP: Bounded, incremental partially-observable Markov model planning
29. High-level planning and control with incomplete information using POMDPs
30. Learning sorting and decision trees with POMDPs
31. Computationally feasible bounds for partially observed markov decision processes
32. An algorithm for probabilistic planning
33. A model for reasoning about persis-tence and causation
34. Exploiting structure in policy construction
35. Fast planning through planning graph analysis
36. Pushing the envelope: Planning, propositional logic, and stochastic search
37. Probabilistic planning with information gathering and contingent execution
38. The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression
39. Planning as model checking
40. Finding optimal solutions to to Rubik's cube using pattern databases
41. The detection and exploitation of symmetry in planning domains
42. Modelling intelligent behaviour: The MDP approach