Principles of artificial intelligence
Principles of artificial intelligence
Modelling social action for AI agents
Artificial Intelligence - Special issue: artificial intelligence 40 years later
An exception-handling architecture for open electronic marketplaces of contract net software agents
Proceedings of the 2nd ACM conference on Electronic commerce
Operational specification of a commitment-based agent communication language
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Automating supply-chain management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
A Knowledge-Based Approach for Designing Robust Business Processes
Business Process Management, Models, Techniques, and Empirical Studies
Models for Supply Chains in E-Business
Management Science
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
Annals of Mathematics and Artificial Intelligence
Modeling exceptions via commitment protocols
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
| Hi-index | 0.00 |
Exceptions take place when one or more events take place unexpectedly. Exceptions occur frequently in supply-chains and mostly result in severe monetary losses. Consequently, detecting exceptions timely is of great practical value. Traditional approaches have aimed at detecting exceptions after they have occurred. Whereas this is important, predicting exceptions before they happen is of more importance, since it can ease the handling of exceptions. Accordingly, this paper develops a commitment-based approach for modeling and predicting exceptions. The participants of the supply-chains are represented as autonomous agents. Their communication with other agents yields creation and manipulation of commitments. Violation of commitments leads to exceptions. We develop two methods for detecting such violations. The first method uses an AND/OR tree to analyze situations in small parts. The second method uses an ontology to generate new information about the environment and checks whether this information may cause any violations. When applied together, these methods can predict exceptions in supply-chain scenarios.