A theory of diagnosis from first principles
Artificial Intelligence
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
Diagnosis of plan step errors and plan structure violations
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Using DESs for Temporal Diagnosis of Multi-agent Plan Execution
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
Diagnosis of Plan Structure Violations
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
Primary and secondary diagnosis of multi-agent plan execution
Autonomous Agents and Multi-Agent Systems
Diagnosis of Simple Temporal Networks
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Diagnosis of multi-agent plan execution
MATES'06 Proceedings of the 4th German conference on Multiagent System Technologies
| Hi-index | 0.00 |
We discuss the application of Model-Based Diagnosis in (agent- based) planning. Here, a plan together with its executing agent is considered as a system to be diagnosed. It is assumed that the execution of a plan can be monitored by making partial observations of the results of actions. These observations are used to explain the observed deviations from the plan by 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: such failings may occur as the result of malfunctioning of the executing agent or may be caused by 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 determine the underlying causes of plan failing and to predict future anomalies in the execution of actions.