Building and Refining Abstract Planning Cases by Change of Representation Language

Bergmann, Ralph; Wilke, Wolfgang

doi:10.1613/jair.160

Cited by 52 publications

(34 citation statements)

References 56 publications

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“…CHEF [31] and DIAL [42] are case-based, but do not have a generative component, and thus need a large case base to perform well across a wide variety of problems. Prodigy/Analogy [71], DerSNLP [33], and Paris [11] integrate generative and case-based planning, but require a complete domain theory and are not mixed-initiative.…”

Section: Work Related To Hicapmentioning

confidence: 99%

Inferencing in Support of Active Templates

Nau¹

2005

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and The primary accomplishments of this project include the following:(1) HICAP is a general purpose planning architecture to assist military commanders with planning NEOs (Noncombatant Evacuation Operations). HICAP integrates a hierarchical task editor with a planning tool. Military planners select a task to decompose in the task editor and then use the planning tool to interactively refine it into an operational plan.(2) SHOP and SHOP2 are some simple, practical planning tools based on HTN planning. Their practical utility is shown by the mergence of an active set of users, which include government laboratories, industrial R&D projects, and academic settings. SHOP2 received one of the top four awards in the 2002 International Planning Competition.(3) The Air Force Research Laboratory's Causal Analysis Tool (CAT) is a system for creating and analyzing causal models similar to Bayesian networks. We have enhanced CAT by developing an approach to quickly generate plans that have high probabilities of success. HICAP. HICAP is a general purpose planning architecture that we have developed and applied to assist military commanders with planning NEOs (Noncombatant Evacuation Operations). HICAP integrates a hierarchical task editor, HTE, with SiN (SHOP in NaCoDAE), a planning tool that tightly integrates conversational case-based planning (through HICAP's NaCoDAE/HTN subsystem) with HTN decomposition (using the SHOP planner described below). In this application, HTE maintains an agenda of tactical planning tasks that, according to military doctrine, must be addressed in a NEO plan. It also supports several bookkeeping tasks, which are crucial for large-scale planning tasks that differ greatly among different NEO operations. Military planners select a task to decompose from HTE and then use SiN to interactively refine it into an operational plan by selecting and applying cases, which represent task decompositions from previous NEO operations. Thus, HICAP helps military commanders by using previous experience to formulate operational plans that are in accordance with NEO doctrine. NUMBER OF PAGESSHOP and SHOP2. SHOP and SHOP2 are HTN planning systems that we designed with two goals in mind: to investigate some research issues in automated planning, and to provide some simple, practical planning tools that could be used to support AcT and also be used elsewhere. We have made them available as open-source software, and they have been dow...

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Section: Work Related To Hicapmentioning

confidence: 99%

Inferencing in Support of Active Templates

Nau¹

2005

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“…9 CBR systems have been successfully used in numerous problem-solving "Paul Cohen called such problems of inference "the mind-reading problem" during an invited presentation at the AAAI-99 Workshop on Mixed-initiative intelligence. 17 tasks and domains; however, the central tasks CBR addresses are those of large-scale interpretation, classification, diagnosis, and explanation of complex situations.…”

Section: Case-based Reasoningmentioning

confidence: 99%

“…9 However, the simplicity of our abstraction scheme allows for the minimal abstraction theory, since we are simply counting a number of occurrences of a literal of a certain type among a collection of literals representing a world state. Because the state abstraction is used as a way to quickly trigger appropriate past memories, this approach does not require a domain expert to specify the abstract language either, besides the object type hierarchy.…”

Section: Related and Future Researchmentioning

confidence: 99%

Incremental Case-Based Plan Recognition With Local Predictions

Kerkez

Cox

2003

Int. J. Artif. Intell. Tools

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We present a novel case-based plan recognition method that interprets observations of plan behavior using an incrementally constructed case library of past observations. The technique is novel in several ways. It combines plan recognition with case-based reasoning and leverages the strengths of both. The representation of a plan is a sequence of action-state pairs rather than only the actions. The technique compensates for the additional complexity with a unique abstraction scheme augmented by pseudo-isomorphic similarity relations to represent indices into the case base. Past cases are used to predict subsequent actions by adapting old actions and their arguments. Moreover, the technique makes predictions despite observations of unknown actions. This paper evaluates the algorithms and their implementation both analytically and empirically. The evaluation criteria include prediction accuracy at both an abstract and a concrete level and across multiple domains with and without case-adaptation. In each domain the system starts with an empty case base that grows to include thousands of past observations. Results demonstrate that this new method is accurate, robust, scalable, and general across domains.

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“…The overall CBR model adopted for this work is best understood by consideration of Figure 2 following (adapted from Bergmann, 1995). While providing a coherent organisation for all the necessary components of a full EBMT system, we presently concentrate on only three components of this model, namely: Case Organisation, Case Retrieval, and Case Adaptation (described in sections 3, 4 and 5 respectively).…”

Section: Overall Architecturementioning

confidence: 99%

Adaptation-guided retrieval in EBMT: A case-based approach to machine translation

Collins¹,

Cunningham²

1996

Lecture Notes in Computer Science

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In this paper we describe a methodological analysis of EBMT (Example-Based Machine Translation) based on a CBR (Case-Based Reasoning) perspective. This analysis focuses on adaptation. We argue that, just as in CBR, the overall power of an EBMT system is its ability to adapt examples retrieved to suit the new problem translation. Here we describe a technique whereby reusability is a function of the abstract "adaptability" information stored in the cases. This information is exploited during both the adaptation and retrieval stages.

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Building and Refining Abstract Planning Cases by Change of Representation Language

Cited by 52 publications

References 56 publications

Inferencing in Support of Active Templates

Inferencing in Support of Active Templates

Incremental Case-Based Plan Recognition With Local Predictions

Adaptation-guided retrieval in EBMT: A case-based approach to machine translation

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