Satisfaction And Its Application To Ai Planning

Dynamic Flexible Constraint Satisfaction and Its Application to AI Planning Ian Miguel AI Group Department of Computer Science University of York

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Constraints ,[object Object],[object Object],[object Object],[object Object],[object Object]

The Constraint Satisfaction Problem (CSP) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Specify allowed combinations of assignments of values to variables.

Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example CSP – Course Scheduling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],These unary constraints determine the domains in this simple example.

Solving CSPs ,[object Object],[object Object],[object Object],[object Object],Root 1 st variable 2 nd variable

Course Scheduling: A Solution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Problem Changes… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Weakness 1: Static Formulation ,[object Object],[object Object],[object Object],[object Object],[object Object]

Dynamic CSP ,[object Object],[object Object],[object Object],[object Object]

Solving Dynamic CSPs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Course Scheduling ,[object Object],[object Object],Problem 1 Problem 2 Solution: Lectures = 4 Exercise = 3 Training = 1 Solution: Lectures = 3 Exercise = 4 Training = 1 ,[object Object],[object Object],[object Object],Incompatible with solution to problem 1

The Problem Changes Again… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Weakness 2: Hard Constraints ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Flexible CSP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Solving Flexible CSPs ,[object Object],[object Object],[object Object],[object Object],[object Object],New bound E.g. 3 violated constraints. Equals bound: 3 violated already. Prune.

Course Scheduling ,[object Object],[object Object],Problem 2 Problem 3 Solution: Lectures = 3 Exercise = 4 Training = 1 ,[object Object],[object Object],Incompatible with solution to problem 2

The Gap in the Market ,[object Object],[object Object],[object Object]

Dynamic Flexible CSP ,[object Object],Flexible CSP Techniques Dynamic CSP Techniques DFCSP Instance Restriction Relaxation Recurrent Activity … Max Weighted Max Weighted Preference Fuzzy … Solution methods

Fuzzy rr DFCSP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Two Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]

Flexible Local Changes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Flexible Local Changes: Operation ,[object Object],[object Object],[object Object],[object Object],1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 … Sub-sub problem solved. Sub-problem solved.

Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experiments: Measurements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results: Search Effort ,[object Object],[object Object],[object Object]

Search Effort: Trends ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Search Effort: Trends ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results: Stability ,[object Object],[object Object]

Stability: Trends ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Utility of Dynamic Information ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Part II Application to AI Planning

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],AI Planning c 1 c 2 c 4 c 3 r 1 r 2 r 3 m 1 m 2 pkg 1 pkg 2 guard 1

Characteristics of AI Planning ,[object Object],[object Object],[object Object],[object Object],[object Object]

Flexible Planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Can relax, with associated damage to resultant plan Imperative Preference

Example ,[object Object],[object Object],[object Object],[object Object],[object Object],c 1 c 2 c 4 c 3 r 1 r 2 r 3 m 1 m 2 pkg 1 pkg 2 guard 1 L={l  , l 1 , l 2 , l T }

Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],c 1 c 2 c 4 c 3 r 1 r 2 r 3 m 1 m 2 pkg 1 pkg 2 guard 1 L={l  , l 1 , l 2 , l T }

Graphplan ,[object Object],[object Object],[object Object],[object Object],[object Object],Initial Conditions Actions 1 Propositions 1 . . . . . . . . . ,[object Object],[object Object],[object Object]

The Flexible Planning Graph ,[object Object],Actions 1 Propositions 1 . . . . . . . . . l 2 l 3 l 1

The CSP Viewpoint ,[object Object],[object Object],[object Object],Actions 1 Propositions 1 . . . . . . . . . l 2 l 3 l 1

Plan Synthesis via Fuzzy rr DFCSP ,[object Object],[object Object],Goal Sub-problem ,[object Object],[object Object],[object Object]

Guiding Overall Search ,[object Object],[object Object],[object Object],Goal Sub-problem

4-step Solution ,[object Object],[object Object],[object Object],[object Object],c 1 c 2 c 4 c 3 r 1 r 2 r 3 m 1 m 2 pkg 1 pkg 2 guard 1 L={l  , l 1 , l 2 , l T } Satisfaction: l 1

6-step Solution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],c 1 c 2 c 4 c 3 r 1 r 2 r 3 m 1 m 2 pkg 1 pkg 2 guard 1 L={l  , l 1 , l 2 , l T } Satisfaction: l 2

Compromise-free Solution ,[object Object],[object Object],[object Object],[object Object],c 1 c 2 c 4 c 3 r 1 r 2 r 3 m 1 m 2 pkg 1 pkg 2 guard 1 L={l  , l 1 , l 2 , l T }

Flexible Graphplan: Observations ,[object Object],[object Object],[object Object],[object Object]

Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Acknowledgements ,[object Object],[object Object]

Satisfaction And Its Application To Ai Planning

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie Satisfaction And Its Application To Ai Planning

Ähnlich wie Satisfaction And Its Application To Ai Planning (20)

Mehr von ahmad bassiouny

Mehr von ahmad bassiouny (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Satisfaction And Its Application To Ai Planning