Intelligent Agents: Technology and Applications Coordination and Planning

1. Intelligent Agents: Technology and ApplicationsCoordination and Planning IST 597B Spring 2003 John Yen

2. Learning Objectives: • Given an agent coordination and planning problem, be able to identify potential conflicts. • Given a potential conflict, be able to design solutions/algorithms for resolving the conflict.

3. An Example of Single Agent Planning • Initial State: On(C A) and OnTable(B) C A B • Goal State: On(A B) and On(B C) A B C

4. Operators: PutOnTable(X): Put X on the table Precondition: Clear(X) Effect: OnTable(X) PutOn(X, Y) : Put X on Y. Precondition: Clear(X) and Clear(Y) Effect: On(X, Y), Not Clear(Y)

5. Linear (STRIPS) Planner Initialize the goal list with all conditions in the goal state Repeat Select a goal from the goal list Find an operator whose effect matches the goal, Add the operator to the front of the current plan (a sequence of actions) For each precondition of the operator, If it is not satisfied by the initial state, add it to the goal list. Until the goal list is empty

6. Problem 1 (5 points): What problems are encountered in applying STRIPS planning to the problem? What causes the problem?

7. The Sussman Anomaly • Introduces the issue of “goal interaction” (i.e., conflicts between actions in a plan) by Gerald Jay Sussman (1974) • Reveals the problem of “linearity assumption” of STRIPS planners • Motivates the second generation planners (nonlinear planners)

8. Clear(C) Clear(B) Clear(A) Clear(B) PutOn(B,C) PutOn(A,B) On(B,C) Clear(C) On(A,B) Clear(B) Initial State Clear (C), On (C,A) On Table(A) OnTable(B) Clear (B) threat Goal State On(B,C) On(A,B) OnTable(C) Clear(A) Clear(B) Clear(C)

9. Conflicts in Plans • A planned action a1 by agent A creates a conflict if • It prevents agent B to perform a planned action a2 (i.e., precondition conflict), or • It interferes with the desired conditions in the goal state (i.e., goal conflict)

10. Detecting Conflicts • Detecting a potential violation of a constraint for protecting • a precondition of an action or • a condition in the goal state.

11. Target of Protection: Causal Dependency • A causal dependency exists between an action A1 and action A2 if the effect of A1 establishes a precondition of A2. • A causal dependency exists between an action A1 and the goal state S if the effect of A1 achieves a goal condition in S.

12. Clear(C) Clear(B) Clear(A) Clear(B) PutOn(B,C) PutOn(A,B) On(B,C) Clear(C) On(A,B) Clear(B) Initial State Clear (C), On (C,A) On Table(A) OnTable(B) Clear (B) threat Goal State On(B,C) On(A,B) OnTable(C) Clear(A) Clear(B) Clear(C)

13. Solution for Handling Threats to Causal Dependency • Promotion: Move the action that creates the threat BEFORE the threatened causal dependency. • Demotion: Move the action that creates the threat AFTER the threatened causal dependency.

14. Clear(C) Clear(B) Clear(A) Clear(B) PutOn(B,C) PutOn(A,B) On(B,C) Clear(C) On(A,B) Clear(B) Initial State Clear (C), On (C,A) On Table(A) OnTable(B) Clear (B) Threat resolved before Goal State On(B,C) On(A,B) OnTable(C) Clear(A) Clear(B) Clear(C)

15. Problem 2: (5 points) Consider the problem described in p. 428 and Fig. 8.24 of the text. • Is this conflict about resource the same as those revealed in the Sussman Anomaly? Explain your answer. • Does the “human” solution described in the book suggest additional conflict resolution schemes (beyond promotion and demotion) in the case of multi-agent planning? If so, describe these additional conflict resolution schemes in multi-agent planning.

16. Solutions for Handling Threats in Multi-agent Planning • Promotion • Demotion • Consider alternative ways to perform a task • Replace actors through • Negotiations • Delegations • Bidding