Case-based planning

Cox, Michael T.; Muñoz-Ávila, Héctor; Bergmann, Ralph

doi:10.1017/s0269888906000592

Cited by 39 publications

(37 citation statements)

References 22 publications

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“…The formula for state similarity, to be utilized in diversity-aware retrieval algorithms as described in the previous section, therefore becomes: (6) As for the plans themselves, the actions they can include are "AttackMove" (moving to a given spot on the map, while attacking any enemy units encountered on the way), "Patrol" (a defensive attitude, consisting of moving between two locations, prepared to fight in case of an enemy invasion attempt), "Move" (moving the unit to specified coordinates) and the self-explanatory "Attack". For the sake of simplicity, we do not take into account any coordinates associated with these moves when computing plan similarity.…”

Section: Methodsmentioning

confidence: 99%

“…m_pb.attackMove(1,13,10); m_pb.move (2,7,8); m_pb.patrol(2, 9, 7); m_pb.move (13,5,9); m_pb.patrol (13,6,10);…”

Section: Figmentioning

confidence: 99%

“…We believe that the introduction of diversity considerations in case-based planning [6,7,8,9], while so far insufficiently explored, can prove equally advantageous and have a significant impact on fields such as interactive planning [10,11]. In interactive planning, the user is presented with a set of planning choices.…”

Section: Introductionmentioning

confidence: 99%

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Case-Based Plan Diversity

Coman

Muñoz-Ávila

2010

Case-Based Reasoning. Research and Development

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Abstract. The concept of diversity was successfully introduced for recommender-systems. By displaying results that are not only similar to a target problem but also diverse among themselves, recommender systems have been shown to provide more effective guidance to the user. We believe that similar benefits can be obtained in case-based planning, provided that diversityenhancement techniques can be adapted appropriately. Our claim is that diversity is truly useful when it refers not only to the initial and goal states of a plan, but also to the sequence of actions the plan consists of. To formalize this characteristic and support our claim, we define the metric of "plan diversity" and put it to test using plans for a real-time strategy game, a domain chosen for the simplicity and clarity of its tasks and the quantifiable results it generates.

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Section: Methodsmentioning

confidence: 99%

“…m_pb.attackMove(1,13,10); m_pb.move (2,7,8); m_pb.patrol(2, 9, 7); m_pb.move (13,5,9); m_pb.patrol (13,6,10);…”

Section: Figmentioning

confidence: 99%

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Case-Based Plan Diversity

Coman

Muñoz-Ávila

2010

Case-Based Reasoning. Research and Development

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“…Many other researchers have applied CBR to planning. The most relevant ones are summarized in [24] and [6]. More recently, CBR has been applied to hierarchical planning such as in HTN-MAKER [17].…”

Section: Related Workmentioning

confidence: 99%

A case-based approach to heuristic planning

2013

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Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learningbased planners try to automatically acquire these domaindependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.

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“…Some general CBR shells, like JColibri [18] have been implemented and distributed. Furthermore, some tools have been implemented for particular types of problems (e.g., case-based planning [9]) and/or particular types of formalisms (e.g., workflows [15]). This paper presents a formalism and a tool of general use for the development of CBR systems based on the representation language RDFS, thus it contributes to filling the theory-application gap in CBR.…”

Section: Introductionmentioning

confidence: 99%

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

Bruneau

Gaillard²,

Lasolle

et al. 2017

Case-Based Reasoning Research and Development

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Abstract. This paper presents SQTRL, a language for transformation rules for SPARQL queries, a tool associated with it, and how it can be applied to retrieval and adaptation in case-based reasoning (CBR). Three applications of SQTRL are presented in the domains of cooking and digital humanities. For a CBR system using RDFS for representing cases and domain knowledge, and SPARQL for its query language, case retrieval with SQTRL consists in a minimal modification of the query so that it matches at least a source case. Adaptation based on the modification of an RDFS base can also be handled with the help of this tool. SQTRL and its tool can therefore be used for several goals related to CBR systems based on the semantic web standards RDFS and SPARQL.

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Case-based planning

Cited by 39 publications

References 22 publications

Case-Based Plan Diversity

Case-Based Plan Diversity

A case-based approach to heuristic planning

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

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