A theory of diagnosis from first principles
Artificial Intelligence
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Autonomous Agents and Multi-Agent Systems
Diagnosing a Team of Agents: Scaling-Up
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
On the design of social diagnosis algorithms for multi-agent teams
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Model-based diagnosis of planning failures
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Plan Diagnosis and Agent Diagnosis in Multi-agent Systems
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Interacting behavioral Petri nets analysis for distributed causal model-based diagnosis
Autonomous Agents and Multi-Agent Systems
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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.