5 research outputs found
Planning as Theorem Proving with Heuristics
Planning as theorem proving in situation calculus was abandoned 50 years ago
as an impossible project. But we have developed a Theorem Proving Lifted
Heuristic (TPLH) planner that searches for a plan in a tree of situations using
the A* search algorithm. It is controlled by a delete relaxation-based domain
independent heuristic. We compare TPLH with Fast Downward (FD) and Best First
Width Search (BFWS) planners over several standard benchmarks. Since our
implementation of the heuristic function is not optimized, TPLH is slower than
FD and BFWS. But it computes shorter plans, and it explores fewer states. We
discuss previous research on planning within KR\&R and identify related
directions. Thus, we show that deductive lifted heuristic planning in situation
calculus is actually doable.Comment: Submitted for a review. Copyright (C) 2023 by Mikhail Soutchanski and
Ryan Youn
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
PLATAS – Integrating Planning and the Action Language Golog
Abstract Action programming languages like Golog allow to define complex behaviors for agents on the basis of action representations in terms of expressive (first-order) logical formalisms, making them suitable for realistic scenarios of agents with only partial world knowledge. Often these scenarios include sub-tasks that require sequential planning. While in principle it is possible to express and execute such planning sub-tasks directly in Golog, the system can performance-wise not compete with state-of-the-art planners. In this paper, we report on our efforts to integrate efficient planning and expressive action programming in the PLATAS project. The theoretical foundation is laid by a mapping between the planning language PDDL and the Situation Calculus, which is underlying Golog, together with a study of how these formalisms relate in terms of expressivity. The practical benefit is demonstrated by an evaluation of embedding a PDDL planner into Golog, showing a drastic increase in performance while retaining the full expressiveness of Golog.