Diagnosis of Plan Execution and the Executing Agent

Nico Roos, Cees Witteveen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


We adapt the Model-Based Diagnosis framework to perform (agent-based) plan diagnosis. In plan diagnosis, the system to be diagnosed is a plan, consisting of a partially ordered set of instances of actions, together with its executing agent. The execution of a plan can be monitored by making partial observations of the results of actions. Like in standard model-based diagnosis, observed deviations from the expected outcomes are explained qualifying some action instances that occur in the plan as behaving abnormally. Unlike in standard model-based diagnosis, however, in plan diagnosis we cannot assume that actions fail independently. We focus on two sources of dependencies between failures: dependencies that arise as a result of a malfunction of the executing agent, and dependencies that arise because of dependencies between action instances occurring in a plan. Therefore, we introduce causal rules that relate health states of the agent and health states of actions to abnormalities of other action instances. These rules enable us to introduce causal set and causal effect diagnoses that use the underlying causes of plan failing to explain deviations and to predict future anomalies in the execution of actions.
Original languageEnglish
Title of host publicationKI 2005: Advances in Artificial Intelligence. KI 2005
EditorsU. Furbach
PublisherSpringer, Berlin, Heidelberg
ISBN (Electronic)978-3-540-31818-7
ISBN (Print)978-3-540-28761-2
Publication statusPublished - 2005

Publication series

SeriesLecture Notes in Computer Science

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