We have been examining mixed-initiative planning systems in the context of command and control or logistical overview situations. In such environments, the human and the computer must work together in a very tightly coupled way to solve problems that neither alone could manage. In this paper, we describe our implementation of a prototype version of such a system, TRAINS-95, which helps a manager solve routing problems in a simple transportation domain. Interestingly perhaps, traditional planning technology does not play a major role in the system, and in fact it is difficult to see how such components might fit into a mixed-initiative system. We describe some of these issues, and present our agenda for future research into mixed-initiative plan reasoning. At this writing, the TRAINS-95 system has been used by more than 100 people to solve simple problems at various conferences and workshops, and in our experiments.

Agents situated in dynamic and uncertain environments require several capabilities for successful operation. Such agents must monitor the world and respond appropriately to important events. The agents should be able to accept goals, synthesize complex plans for achieving those goals, and execute the plans while continuing to be responsive to changes in the world. As events render some current activities obsolete, the agents should be able to modify their plans while continuing activities unaffected by those events. The Cypress system is a domain-independent framework for defining persistent agents with this full range of behavior. Cypress has been used for several demanding applications, including military operations, real-time tracking, and fault diagnosis. ii Contents 1 Introduction 1 1.1 Research Strategy : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 A New Technology : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 2 Overview of C...

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...

Planning Cases by Change of Representation Language Ralph Bergmann bergmann@informatik.uni-kl.de Wolfgang Wilke wilke@informatik.uni-kl.de Centre for Learning Systems and Applications (LSA) University of Kaiserslautern, P.O.-Box 3049, D-67653 Kaiserslautern, Germany Abstract Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of th...

AI planning systems have traditionally operated as stand-alone blackboxes, taking a description of a domain and a set of goals, and automatically synthesizing a plan for achieving those goals. Such designs severely restrict the influence that users can have on the resultant plans. This paper describes an Advisable Planner framework that marries an advice-taking interface to AI planning technology. The framework is designed to enable users to interact with planning systems at high levels of abstraction in order to influence the plan generation process in terms that are meaningful to them. Advice consists of taskspecific constraints on both the desired solution and the refinement decisions that underlie the planning process. The paper emphasizes strategic advice, which expresses recommendations on how goals and actions are to be accomplished. The main contributions are a formal language and semantics for strategic advice, and a sound and complete HTNstyle algorithm for generating plans ...

One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a case’s adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional, similarity assumption in an effort to deliver more sophisticated and scaleable reasoning systems. Smyth & Keane 3 Adaptation-Guided Retrieval 1

In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong locality of mutex constraints observed in many benchmarks of the Fourth International Planning Competition (IPC4), we propose to partition the constraints of a planning problem into groups based on their subgoals. Constraint partitioning leads to significantly easier subproblems that are similar to the original problem and that can be efficiently solved by the same planner with some modifications to its objective function. We present a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subproblems and that resolves those inconsistent global constraints across the subproblems. We also discuss some implementation details of SGPlan4, which include the resolution of violated global constraints, techniques for handling producible resources, landmark analysis, path finding and optimization, search-space reduction, and modifications of Metric-FF when used as a basic planner in SGPlan4. Last, we show results on the sensitivity of each of these techniques in quality-time trade-offs and experimentally demonstrate that SGPlan4 is effective for solving the IPC3 and IPC4 benchmarks. 1.

Practical scheduling problems generally require allocation of resources in the presence of a large, diverse and typically conflicting set of constraints and optimization criteria. The ill-structuredness of both the solution space and the desired objectives make scheduling problems difficult to formalize. This paper describes a case-based learning method for acquiring context-dependent user optimization preferences and tradeoffs and using them to incrementally improve schedule quality in predictive scheduling and reactive schedule management in response to unexpected execution events. The approach, implemented in the CABINS system, uses acquired user preferences to dynamically modify search control to guide schedule improvement. During iterative repair, cases are exploited for: (1) repair action selection, (2) evaluation of intermediate repair results and (3) recovery from revision failures. The method allows the system to dynamically switch between repair heuristic actions, each of whi...

ion in Case-Based Reasoning Ralph Bergmann and Wolfgang Wilke University of Kaiserslautern, Centre for Learning Systems and Applications (LSA) Dept. of Computer Science, P.O.-Box 3049, D-67653 Kaiserslautern, Germany E-Mail: fbergmann,wilkeg@informatik.uni-kl.de Abstract. This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We argue that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation....

We have constructed a team of intelligent agents that perform the tasks of an attack helicopter company for a synthetic battlefield environment used for running largescale military exercises. We have used the Soar integrated architecture to develop: (1) pilot agents for a company of helicopters, (2) a command agent that makes decisions and plans for the helicopter company, and (3) an approach to teamwork that enables the pilot agents to coordinate their activities in accomplishing the goals of the company. This case study describes the task domain and architecture of our application, as well as the benefits and lessons learned from applying AI technology to this domain.