1 / 12

CPSC 7373: Artificial Intelligence Lecture 9: Planning

CPSC 7373: Artificial Intelligence Lecture 9: Planning. Jiang Bian, Fall 2012 University of Arkansas at Little Rock. Planning. We defined AI to be the study and process of finding appropriate actions for an agent . We have looked at problem solving search over a state space.

rosalyn-peterson
Télécharger la présentation

CPSC 7373: Artificial Intelligence Lecture 9: Planning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CPSC 7373: Artificial IntelligenceLecture 9: Planning Jiang Bian, Fall 2012 University of Arkansas at Little Rock

  2. Planning • We defined AI to be the study and process of finding appropriate actions for an agent. • We have looked at problem solving search over a state space. • Given a state space and a problem description, we can find a solution, a path to the goal. • Problem solving approaches only work when the environment is deterministic and fully observable.

  3. Problem Solving vs Planning

  4. A Mystery: Why Can't We Walk Straight? Walking Straight into Circles, by Souman et. al.

  5. Planning vs Execution • Why we need to interleave planning with execution? • Properties of the environment make it hard • STOCHASTIC: We don't know for sure what an action is going to do • MULTIAGENT: • PARTIAL OBSERVABILITY: • Unknown model: lack of knowledge of the world • e.g., we have map or GPS software that's inaccurate or incomplete • Hierarchical: devils in the details • Instead of planning in the space of world states, we plan in the space of belief states.

  6. Vacuum Cleaner Example Search in the state space of belief states rather than in the state space of actual spaces

  7. SensorlessVaccum Cleaner World L L R R

  8. Partially Observable Vacuum Cleaner Observations alone can't introduce a new state [B, Dirty] R [B, Clean] Suppose we have what's called local sensing, that is our vacuum can see what location it is in and it can see what's going on in the current location, that is whether there's dirt in the current location or not, but it can't see anything about whether there's dirt in any other location.

  9. Stochastic Environment [B, Dirty] R [A, Dirty] [B, Clean] Action increase uncertainty, Observation decreases uncertainty

  10. Infinite Sequences • e.g., [S,R,S] S R B S A • e.g., [S, while A:R, S]

  11. CLASSICAL PLANNING • STATE SPACE: k-Boolean (2^k) • WORLD STATE: Complete assignment • BELIEF STATE: • Complete assignment • Partial assignment • Arbitrary formula • ACTION SCHEMA • Action(FLY(p, x, y) • PRECOND: Plane (p) ^ Airport (x) ^ Airport (y) ^ At(p,x) • EFFECT: -At(p,x) ^ At(p,y) • )

  12. SEARCH in Planning • Progression Search (Forward search) • Searching in Problem Solving • Init State -> Goal State • Regression Search (Backward search) • Goal State -> Init State • Progression vs Regression • e.g., • Action(Buy(b), • PRE: ISBN(b) • EFF: OWN(b)) • GOAL (OWN(0136042597)) • Plan Space Search: Searching in the plan space rather than in the world state space.

More Related