1. 5/10/2002 How to describe Plan Recognition Plan Recognizer With Knowledge Observations Assumptions Plans explaining observations

2. 5/10/2002 A Formal Theory of Plan Recognition and its Implementation Henry A. Kautz, 1991

3. 5/10/2002 How to Represent Knowledge? Event Hierarchy A compact representation of a set of first- order logic statements. Two relations in the graph: abstraction and direct component

4. 5/10/2002 Event Hierarchy End Event Make Pasta DishMake Meat Dish Make Spaghetti Pesto Make Spaghetti Marinara Make Sauce Make Chicken Marinara Make Marinara Make Pesto Make Spaghetti Make Noodle

5. 5/10/2002 What Assumptions Are Needed? Exhaustive and Disjointness Assumption: all events and abstraction relations have been expressed in the graph Component/Use Assumption: a non-End event implies only the plans which use the event as a component are possible. Minimum Cardinality Assumption: if we can use one plan to explain all the observations, don’t use two plans.

6. 5/10/2002 Reasoning Directing Inference: from each observation, apply component/use assumption and the abstraction relations until an End event is reached. Then try to reduce the number of alternatives by checking constraints locally. Complexity: exponential to the size of the event hierarchy and observations (?)

7. 5/10/2002 A Sound and Fast Goal Recognizer Neal Lesh and Oren Etzioni, 1995

8. 5/10/2002 How to Represent Knowledge A set of statements of first order logic. The knowledge is used to eliminate inconsistent plans in consistency graph. A plan is consistent with the observations iff the observations can be executed as part of the plan. Informally, the consistency graph represents the plans the actor might be executing. No concept of abstraction in the graph.

9. 5/10/2002 Consistency Graph cd /papersgrep motivating paper.tex

10. 5/10/2002 What Assumptions Are Needed? An actor constructs plans without any irrelevant actions.

11. 5/10/2002 Reasoning Initialize a consistency graph. Use some rules to eliminate those goals not consistent with all the observations. Repeat until no rules can eliminate anything. The algorithm is sound because it guarantees all the consistent goals will remain in the graph The algorithm is incomplete because it does not guarantee eliminate all the inconsistent goals. The algorithm is fast because it needs only polynomial time.