Failure Prediction based on Monitoring Sequences of Actions and Action Duration

• Giovani Farias PUCRS
• Ramon Fraga Pereira PUCRS
• Lucas Hilgert PUCRS
• Felipe Meneguzzi PUCRS
• Renata Vieira PUCRS
• Rafael H. Bordini PUCRS

DOI: https://doi.org/10.5753/wesaac.2016.33207

Resumo

An agent can attempt to achieve multiple goals and each goal can be achieved by applying various different plans. Anticipating failures in agent plan execution is important to enable an agent to develop strategies to avoid or circumvent such failures, allowing the agent to achieve its goal. Plan recognition can be used to infer which plans are being executed from observations of sequences of activities being performed by an agent. Symbolic Plan Recognition is an algorithm that represents knowledge about the agents under observation in the form of a plan library. In this paper, we use this symbolic algorithm to find out which plan the agent is performing and we develop a failure prediction system, based on information available in the plan library and in a simplified calendar which manages the goals the agent has to achieve. This failure predictor is able to monitor the sequence of agent actions and detects if an action is taking too long or does not match the plan that the agent was expected to be performing.

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