Opportunism is an intentional behavior that takes advantage of knowledge asymmetry and results in promoting agents’ own value and demoting others’ value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents’ opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. Agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents’ state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.

An intelligent system for automated scenario-based training (SBT) needs knowledge about the training domain, events taking place in the simulated environment, the behaviour of the participating characters, and teaching strategies for effective learning. This knowledge base should be theoretically sound and should represent the information in a generic, consistent, and unambiguous manner. Currently, there is no such knowledge base. This paper investigates the declarative knowledge needed for a system to reason about training and to make intelligent teaching decisions. A frame-based approach was used to model the identified knowledge in an ontology. The ontology specifies the core concepts of SBT and their relationships, and is applicable across training domains and applications. The ontology was used to develop a critical component of SBT: The scenario generator. It was found that the ontology enabled the scenario generator to develop scenarios that fitted the learning needs and skill level of the trainee. The presented work is an important step towards automated scenario-based training systems.