Efficient Open World Reasoning for Planning

We consider the problem of reasoning and planning with incomplete knowledge and deterministic actions. We introduce a knowledge representation scheme called PSIPLAN that can effectively represent incompleteness of an agent's knowledge while allowing for sound, complete and tractable entailment in domains where the set of all objects is either unknown or infinite. We present a procedure for state update resulting from taking an action in PSIPLAN that is correct, complete and has only polynomial complexity. State update is performed without considering the set of all possible worlds corresponding to the knowledge state. As a result, planning with PSIPLAN is done without direct manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP planning algorithm that handles quantified goals with or without exceptions that no other domain independent planner has been shown to achieve. PSIPLAN has been implemented in Common Lisp and used in an application on planning in a collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer Scienc

Abstract

In traditional human-computer interfaces, a human master directs a computer system as a servant, telling it not only what to do, but also how to do it. Collaborative interfaces attempt to realign the roles, making the participants collaborators in solving the person’s problem. This paper describes Writer’s Aid, a system that deploys AI planning techniques to enable it to serve as an author’s collaborative assistant. Writer’s Aid differs from previous collaborative interfaces in both the kinds of actions the system partner takes and the underlying technology it uses to do so. While an author writes a document, Writer’s Aid helps in identifying and inserting citation keys and by autonomously finding and caching potentially relevant papers and their associated bibliographic information from various on-line sources. This autonomy, enabled by the use of a planning system at the core of Writer’s Aid, distinguishes this system from other collaborative interfaces. The collaborative design and its division of labor result in more efficient operation: faster and easier writing on the user’s part and more effective information gathering on the part of the system. Subjects in our laboratory user study found the system effective and the interface intuitive and easy to use. 1

Abstract

In traditional human-computer interfaces, a human master directs a computer system as a servant, telling it not only what to do, but also how to do it. Collaborative interfaces attempt to realign the roles, making the participants collaborators in solving the person’s problem. This paper describes Writer’s Aid, a system that deploys AI planning techniques to enable it to serve as an author’s collaborative assistant. Writer’s Aid differs from previous collaborative interfaces in both the kinds of actions the system partner takes and the underlying technology it uses to do so. While an author writes a document, Writer’s Aid helps in identifying and inserting citation keys and by autonomously finding and caching potentially relevant papers and their associated bibliographic information from various on-line sources. This autonomy, enabled by the use of a planning system at the core of Writer’s Aid, distinguishes this system from other collaborative interfaces. The collaborative design and its division of labor result in more efficient operation: faster and easier writing on the user’s part and more effective information gathering on the part of the system. Subjects in our laboratory user study found the system effective and the interface intuitive and easy to use. 1

Planning with Incomplete Knowledge and Limited Quantification

We present a new method for partial order planning in the STRIPS/SNLP style. Our contribution centers on how we drop the closed world assumption while adding a useful class of universally quantified propositions to the representation of states and actions. These quantified expressions allow expression of partially closed worlds, such as "block A has no other block on it", or "F is the only Tex file in directory D." In addition, we argue informally that the time complexity of our algorithm is no worse than traditional partial order planners that make the closed world assumption. STRIPS-style planning (Fikes & Nilsson 1971) is decidable only if we restrict the language to finitely many ground terms (Erol, Nau, & Subrahmanian 1992). STRIPS-style planning becomes NP-complete only when we bound the length of the plan being sought (Gupta & Nau 1991). Thus, planning is intractable in the general case. However, thanks to recent advances in applying stochastic search to propositional satisfiab..