Case‐Based Planning: A Framework for Planning from Experience

Hammond, Kristian J.

doi:10.1207/s15516709cog1403_3

Cited by 151 publications

(88 citation statements)

References 10 publications

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“…The step (3) can be performed by a revision operator. For instance, in [14], the well-known example of "beef and broccoli adaptation" of the CHEF system [22] is re-described using the revision operator • D . This also shows, more generally, that revision operators can be used in various ways as tools for designing and implementing adaptation processes.…”

Section: Discussionmentioning

confidence: 99%

Application of the Revision Theory to Adaptation in Case-Based Reasoning: The Conservative Adaptation

Lieber

Case-Based Reasoning Research and Development

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Abstract. Case-based reasoning aims at solving a problem by the adaptation of the solution of an already solved problem that has been retrieved in a case base. This paper defines an approach to adaptation called conservative adaptation; it consists in keeping as much as possible from the solution to be adapted, while being consistent with the domain knowledge. This idea can be related to the theory of revision: the revision of an old knowledge base by a new one consists in making a minimal change on the former, while being consistent with the latter. This leads to a formalization of conservative adaptation based on a revision operator in propositional logic. Then, this theory of conservative adaptation is confronted to an application of case-based decision support to oncology: a problem of this application is the description of a patient ill with breast cancer, and a solution, the therapeutic recommendation for this patient. Examples of adaptations that have actually been performed by experts and that can be captured by conservative adaptation are presented. These examples show a way of adapting contraindicated treatment recommendations and treatment recommendations that cannot be applied.

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Section: Discussionmentioning

confidence: 99%

Application of the Revision Theory to Adaptation in Case-Based Reasoning: The Conservative Adaptation

Lieber

Case-Based Reasoning Research and Development

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“…We have mentioned before the HACKER system [253] that re-uses plans in a library, if possible. More advanced case-based planners, for instance CHEF [127] and DIAL [167], are very popular in domains where domain knowledge, respectively a solution quality metric is missing. In this context, we distinguish "solutions" and "solutions that are accepted by users".…”

Section: Other Planning Techniquesmentioning

confidence: 99%

Hybrid Planning and Scheduling

Schattenberg¹

2015

Künstl Intell

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PrefacePlanning and scheduling are well-established disciplines in the field of Artificial Intelligence. They provide flexibility, robustness, and effectiveness to complex software systems in a variety of application areas. While planning is the process of finding a course of action that achieves a goal or performs a specified task, scheduling deals with the assignment of resources and time to given activities, taking into account resource restrictions and temporal dependencies. In other words, planning focuses on reasoning about causal structures and identifying the necessary actions for achieving a specific goal; scheduling concentrates on resource consumption and production for optimizing a coherent parameter assignment of a plan. As successful these techniques clearly are, the actual demands of complex, real-world applications go far beyond the potential of these single methods, however. They require an adequate integration of these problem solving methods as well as a combination of different planning and scheduling paradigms. Particularly important are abstraction-based, hierarchical approaches because of both their expressive knowledge representation and their efficiency in finding solutions. Current state-of-the-art systems rarely address the question of method integration; isolated approaches do so only in ad hoc implementations and mostly lack a proper formal basis.This thesis presents a formal framework for plan and schedule generation based on a well-founded conceptualization of refinement planning: An abstract problem specification is transformed stepwise into a concrete, executable solution. In each refinement step, plan deficiencies identify faulty or under-developed parts of the plan, which in turn triggers the generation of transformation operators that try to resolve them. All involved entities are explicitly represented and therefore transparent to the framework. This property allows for two novel aspects of our approach: First, any planning and scheduling methodology can be functionally decomposed and mapped on the deficiency announcement and plan transformation generation principle, and second, the framework allows for an explicit declaration of planning strategies. We first investigate the flexibility of the extremely modular system design by instantiating the framework in a variety of system configurations including classical partial-order causal-link (POCL) planning, hierarchical task-network (HTN) planning, and classical scheduling.As a key feature, the presented approach provides a formally integrated treatment of action and state abstraction, thus naturally combining causality-focused reasoning with hierarchical, procedure-oriented methods. While the use of procedural knowledge allows to rely on well-known, predefined solutions to planning problems, the non-hierarchical methods provide the flexibility to come up with non-standard solutions and to complete under-specified problem instances, respectively. The resulting technique of hybrid planning is capable of constructing a plan's caus...

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“…In general, though, a more refined indexing scheme, such as indexing by differences [21,22] or based upon the footprint metric [38] or based upon causal analysis [16] might be needed to keep retrieval times manageable. Further refinements such as distinguishing between short and long term memory or selectively "forgetting" past cases [34] may also be fruitful.…”

Section: Retrieving Coordinated Procedures From the Casebasementioning

confidence: 99%

Autonomous Agents that Learn to Better Coordinate

Garland

Alterman

2004

Autonomous Agents and Multi-Agent Systems

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A fundamental difficulty faced by groups of agents that work together is how to efficiently coordinate their efforts. This coordination problem is both ubiquitous and challenging, especially in environments where autonomous agents are motivated by personal goals.Previous AI research on coordination has developed techniques that allow agents to act efficiently from the outset based on common built-in knowledge or to learn to act efficiently when the agents are not autonomous. The research described in this paper builds on those efforts by developing distributed learning techniques that improve coordination among autonomous agents.The techniques presented in this work encompass agents who are heterogeneous, who do not have complete built-in common knowledge, and who cannot coordinate solely by observation. An agent learns from her experiences so that her future behavior more accurately reflects what works (or does not work) in practice. Each agent stores past successes (both planned and unplanned) in their individual casebase. Entries in a casebase are represented as coordinated procedures and are organized around learned expectations about other agents.It is a novel approach for individuals to learn procedures as a means for the group to coordinate more efficiently. Empirical results validate the utility of this approach. Whether or not the agents have initial expertise in solving coordination problems, the distributed learning of the individual agents significantly improves the overall performance of the community, including reducing planning and communication costs.

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Case‐Based Planning: A Framework for Planning from Experience

Cited by 151 publications

References 10 publications

Application of the Revision Theory to Adaptation in Case-Based Reasoning: The Conservative Adaptation

Application of the Revision Theory to Adaptation in Case-Based Reasoning: The Conservative Adaptation

Hybrid Planning and Scheduling

Autonomous Agents that Learn to Better Coordinate

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