Initial state prediction in planning

Senka Krivic; Michael Cashmore; Bram Ridder; Justus Piater

Initial state prediction in planning

Senka Krivic, Michael Cashmore, Bram Ridder, Justus Piater

Computer And Information Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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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.

Original language	English
Title of host publication	The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning
Place of Publication	Palo Alto, US-CA.
Publication status	Published - 21 Mar 2017

Keywords

reasoning techniques
online execution strategies
contingency planning
artifical intelligence
AI

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title = "Initial state prediction in planning",

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.",

keywords = "reasoning techniques, online execution strategies, contingency planning, artifical intelligence, AI",

author = "Senka Krivic and Michael Cashmore and Bram Ridder and Justus Piater",

year = "2017",

month = mar,

day = "21",

language = "English",

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T1 - Initial state prediction in planning

AU - Krivic, Senka

AU - Cashmore, Michael

AU - Ridder, Bram

AU - Piater, Justus

PY - 2017/3/21

Y1 - 2017/3/21

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.

KW - reasoning techniques

KW - online execution strategies

KW - contingency planning

KW - artifical intelligence

KW - AI

UR - https://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15157

M3 - Conference contribution book

BT - The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning

CY - Palo Alto, US-CA.

ER -

Initial state prediction in planning

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Keywords

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