Action Graphs for Goal Recognition Problems with Inaccurate Initial States (Student Abstract)

Harman, Helen and Simoens, Pieter (2020) Action Graphs for Goal Recognition Problems with Inaccurate Initial States (Student Abstract). In: The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20).

Full content URL: https://doi.org/10.1609/aaai.v34i10.7174

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Goal recognisers attempt to infer an agent's intentions from a sequence of observations. Approaches that adapt classical planning techniques to goal recognition have previously been proposed but, generally, they assume the initial world state is accurately defined. In this paper, a state is inaccurate if any fluent's value is unknown or incorrect. To cope with this, a cyclic Action Graph, which models the order constraints between actions, is traversed to label each node with their distance from each hypothesis goal. These distances are used to calculate the posterior goal probabilities. Our experimental results, for 15 different domains, demonstrate that our approach is unaffected by an inaccurately defined initial state.

Keywords:goal recognition
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:44710
Deposited On:30 Apr 2021 11:15

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