Initial state prediction in planning

Senka Krivic, Michael Cashmore, Bram Ridder, Justus Piater

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multigraph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.
LanguageEnglish
Title of host publicationThe AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning
Place of PublicationPalo Alto, US-CA.
Publication statusPublished - 21 Mar 2017

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Planning
Uncertainty

Keywords

  • reasoning techniques
  • online execution strategies
  • contingency planning
  • artifical intelligence
  • AI

Cite this

Krivic, S., Cashmore, M., Ridder, B., & Piater, J. (2017). Initial state prediction in planning. In The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning Palo Alto, US-CA..
Krivic, Senka ; Cashmore, Michael ; Ridder, Bram ; Piater, Justus. / Initial state prediction in planning. The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning. Palo Alto, US-CA., 2017.
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author = "Senka Krivic and Michael Cashmore and Bram Ridder and Justus Piater",
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Krivic, S, Cashmore, M, Ridder, B & Piater, J 2017, Initial state prediction in planning. in The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning. Palo Alto, US-CA.

Initial state prediction in planning. / Krivic, Senka; Cashmore, Michael; Ridder, Bram; Piater, Justus.

The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning. Palo Alto, US-CA., 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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AU - Ridder, Bram

AU - Piater, Justus

PY - 2017/3/21

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N2 - While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multigraph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.

AB - While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multigraph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.

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KW - online execution strategies

KW - contingency planning

KW - artifical intelligence

KW - AI

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Krivic S, Cashmore M, Ridder B, Piater J. Initial state prediction in planning. In The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning. Palo Alto, US-CA. 2017