Predicting Plan Failure by Monitoring Action Sequences and Duration
| Autor: | Felipe Meneguzzi, Rafael H. Bordini, Lucas Hilgert, Ramon Fraga Pereira, Renata Vieira, Giovani Parente Farias |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Plan recognition 02 engineering and technology Plan (drawing) plan recognition Informótica Prediction system lcsh:QA75.5-76.95 020204 information systems Monitoring - action multi-agent system 0202 electrical engineering electronic engineering information engineering failure prediction Duration (project management) General Environmental Science business.industry Computing General Engineering Computación Action (philosophy) General Earth and Planetary Sciences 020201 artificial intelligence & image processing Plan library lcsh:Electronic computers. Computer science Artificial intelligence Information Technology Symbolic algorithm Software engineering business |
| Zdroj: | GREDOS. Repositorio Institucional de la Universidad de Salamanca instname Advances in Distributed Computing and Artificial Intelligence Journal, Vol 6, Iss 4, Pp 55-69 (2018) Advances in Distributed Computing and Artificial Intelligence Journal, Vol 6, Iss 2, Pp 71-84 (2017) |
| ISSN: | 2255-2863 |
| DOI: | 10.14201/adcaij2017627184 |
| Popis: | 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 work, 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. We have successfully employed this approach in a health-care prototype system. |
| Databáze: | OpenAIRE |
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