The ability of a planner to reuse parts of old plans is hypothesized to be a valuable tool for improving efficiency of planning by avoiding the repetition of the same planning effort. We test this hypothesis from an analytical and empirical point of view. A comparative worst-case complexity analysis of generation and reuse under different assumptions reveals that it is not possible to achieve a provable efficiency gain of reuse over generation. Further, assuming "conservative" plan modification, plan reuse can actually be strictly more difficult than plan generation. While these results do not imply that there won't be an efficiency gain in some situations, retrieval of a good plan may present a serious bottleneck for plan reuse systems, as we will show. Finally, we present the results of an empirical study of two different plan reuse systems, pointing out possible pitfalls one should be aware of when attempting to employ reuse methods. ? This work was supported by the German Ministry...

this article).

. After a short analysis of the requirements that a knowledge representation language must satisfy, we introduce Description Logics, Modal Logics, and Nonmonotonic Logics as formalisms for representing terminological knowledge, time-dependent or subjective knowledge, and incomplete knowledge respectively. At the end of each section, we briefly comment on the connection to Logic Programming. 1 Introduction This section is concerned with the question under which conditions one may rightfully claim to have represented knowledge about an application domain, and not just stored data occurring in this domain. 1 In the early days of Artificial Intelligence and Knowledge Representation, there was a heated discussion on whether logic can at all be used as a formalism for Knowledge Representation (see e.g. [135, 91, 92]). One aspect of the requirements on knowledge representation formalisms that can be derived from the considerations in this section is very well satisfied by logical for...

Planning from second principles by reusing and modifying plans is one way of improving the efficiency of planning systems. In this paper, we study it in the general framework of deductive planning and develop a logical formalization of planning from second principles, which relies on a systematic decomposition of the planning process. Deductive inference processes with clearly defined semantics formalize each of the subtasks a second principles planner has to address. Plan modification, which comprises matching and adaptation tasks, is based on a deductive approach yielding provably correct modified plans. Description logics are introduced as query languages to plan libraries, which leads to a novel and efficient solution to the indexing problem in case-based reasoning. Apart from sequential plans, this approach enables a planner to reuse and modify complex plans containing control structures like conditionals and loops. 1 Introduction Planning from first principles generat...

. The reuse of plans is widely considered as a valuable tool for the improvement of efficiency in planning systems because it avoids the repetition of planning effort. Several approaches which investigate the modification and reuse of sequential plans in the framework of strips-based planning have been developed. In this paper we present a domain-independent approach to flexible plan reuse based on a deductive framework. A formalization of the whole reuse process is proposed including the modification, representation and retrieval of plans. Plan modification is based on a semantic approach which yields provably correct modified plans. The plan library is represented in a hybrid knowledge representation formalism linking the planning logic with a terminological logic and is dynamically created and maintained by the system requiring no user interaction. Apart from sequential plans, this approach enables a planner to reuse and modify plans containing control structures like conditionals a...

Case-based planning is the reuse of past successful plans in order to solve new planning problems. This paper presents a survey of case-based planning, in terms of its historical roots, underlying foundations, methods and techniques currently used, limitations, and future trends. Several authors have given overviews on case-based reasoning and specific topics such as case retrieval, case adaptation, and learning. This overview differs in focus. Its aim is to emphasize the case-based approach to planning, its methodological issues, and its relation to classical planning and the other kinds of case-based reasoning. It also provides some reference models. Keywords: Case-based planning, Case-based reasoning, Planning, Plan Retention, Plan Retrieval, Plan Reuse, Plan Revision. Statement of Exclusive Submission: This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Artificial Intelligence Review. 1 Contents 1

The paper describes an approach to reasoning about actions and plan generation within the framework of description logics. From an epistemological viewpoint, our approach is based on the formalization of actions given by dynamic logics, but we exploit their correspondence with description logics to turn the formalization into an actual implementation. In particular, we are able to carefully weaken the logical inference process, thus making the reasoning of the robot computationally feasible. From a practical viewpoint, we use a general purpose knowledge representation environment based on description logics, and its associated reasoning tools, in order to plan the actions of the mobile robot "Tino", starting from the knowledge about the environment and the action specification. The robot's reactive capabilities allow it to execute such plans in the real world.

Case-based planning is considered as a valuable tool for improving efficiency in planning by reuse and modification of existing plans. In this paper, the results of an empirical study are discussed in which several factors influencing case-based planning are investigated. The results demonstrate relative efficiency gains or losses caused by different refitting strategies, different types of plans or typical properties of the application domain and identify possible pitfalls for case-based planning. Introduction Recently, several complexity theoretic studies have been performed investigating worst and average cases. The comparative worst-case complexity analysis of generation and reuse under different assumptions reveals that it is not possible to prove an efficiency gain of reuse over generation (Nebel & Koehler 1993a; 1993b). The average case analysis shows that plan modification can be more efficient than plan generation under very restrictive assumptions (Bylander 1993). Neverthele...

We discuss a rigorous unifying framework for both planning and replanning, extending an existing logic-based approach to resource-based planning. The primitive concepts in this Action Resource Framework (ARF) are actions and resources. Actions consume and produce resources. Plans are structures composed of actions, resource facts and an explicit dependency function specifying their interrelationships. In this framework, both planning and replanning are conceived as plan transformation processes accomplished by applying sequences of operations on plans. For this, we introduce operators for plan transformation and define the concept of a plan library. Using a refinement planning template, we show how some existing (re)planning methods and heuristics can be described as special cases of this framework. The advantage of the framework is that it offers a unifying view on planning and replanning. 1.

We describe an approach to reasoning agents which is based on a formal theory of actions and is actually implemented on a mobile robot working in an office environment. From an epistemological viewpoint, our proposal is originated by the correspondence between Dynamic Logics and Description Logics. Specifically, we consider an epistemic extension of Description Logics to provide a new theoretical framework for the representation of dynamic systems, where the agent's reasoning is based on its knowledge about the world. In this setting, we obtain a weaker notion of logical inference, thus simplifying the reasoning task. From a practical viewpoint, we use a general purpose knowledge representation system based on Description Logics and its associated reasoning tools, in order to plan the actions of the mobile robot "Tino", starting from the knowledge about the environment and the action specification. In addition, we exploit the robot's capabilities in order to integrate the execution of ...