Cost-based goal recognition in navigational domains

Masters, P and Sardina, S 2019, 'Cost-based goal recognition in navigational domains', Journal of Artificial Intelligence Research, vol. 64, pp. 197-242.

Document type: Journal Article
Collection: Journal Articles

Title Cost-based goal recognition in navigational domains
Author(s) Masters, P
Sardina, S
Year 2019
Journal name Journal of Artificial Intelligence Research
Volume number 64
Start page 197
End page 242
Total pages 46
Publisher A A A I Press
Abstract Goal recognition is the problem of determining an agent's intent by observing her behaviour. Contemporary solutions for general task-planning relate the probability of a goal to the cost of reaching it. We adapt this approach to goal recognition in the strict context of path-planning. We show (1) that a simpler formula provides an identical result to current state-of-the-art in less than half the time under all but one set of conditions. Further, we prove (2) that the probability distribution based on this technique is independent of an agent's past behaviour and present a revised formula that achieves goal recognition by reference to the agent's starting point and current location only. Building on this, we demonstrate (3) that a Radius of Maximum Probability (i.e., the distance from a goal within which that goal is guaranteed to be the most probable) can be calculated from relative cost-distances between the candidate goals and a start location, without needing to calculate any actual probabilities. In this extended version of earlier work, we generalise our framework to the continuous domain and discuss our results, including the conditions under which our findings can be generalised back to goal recognition in general task-planning.
Subject Adaptive Agents and Intelligent Robotics
DOI - identifier 10.1613/jair.1.11343
Copyright notice © 2019 AI Access Foundation. All rights reserved.
ISSN 1076-9757
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