Combining Symbolic and Statistical Knowledge for Goal Recognition in Smart Home Environments

Wilken, Nils; Stuckenschmidt, Heiner

doi:10.1109/percomworkshops51409.2021.9431145

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…This paper is based on simulation, but the proposed algorithm has more realworld application scenarios. The algorithm provides ideas not only for adversarial game scenarios, but also for other multi-agent scenarios involving complex temporal logic tasks such as goal recognition in smart home environments [31,32]. In the future, we will apply the algorithm to plan recognition in realistic real-time strategy games.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Plan Recognition for Multi-Agent Systems under Temporal Logic Tasks

Tian

et al. 2022

Electronics

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This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the future goals and identify the temporal logic tasks of the agent based on the observations of their states and actions. We subsequently build a plan library composed of Nondeterministic Bu¨chi Automation to model the temporal logic tasks. We also propose a Boolean matrix generation algorithm to map the plan library to multi-agent trajectories and a task recognition algorithm to parse the Boolean matrix. Then, the probability calculation formula is proposed to calculate the posterior goal probability distribution, and the cold start situation of the plan recognition is solved using the Bayes formula. Finally, we validate the proposed algorithm via extensive comparative simulations.

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Section: Discussionmentioning

confidence: 99%