Formal theories of knowledge in AI and robotics
New Generation Computing
Knowledge and common knowledge in a distributed environment
Journal of the ACM (JACM)
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
Deliberation and its role in the formation of intentions
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A guide to completeness and complexity for modal logics of knowledge and belief
Artificial Intelligence
Interval structure: a framework for representing uncertain information
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Cooperation under uncertainty in distributed expert systems
Artificial Intelligence
Artificial Intelligence
A situated view of representation and control
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Reasoning about knowledge
Modeling agents as qualitative decision makers
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Modeling belief in dynamic systems, part I: foundations
Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Uncertainty management in a distributed knowledge based system
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
On the foundations of qualitative decision theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Plausibility measures: a user's guide
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
One of the important characteristics for intelligent agents is to be able to assess their environments in order to decide on the correct action to take. It is always difficult to do so because many factors including uncertain information, knowledge and bounded time will affect intelligent agent to perceive their environments. In this paper, we propose a procedure descriptive framework to perceive the environments for intelligent agents. The process of belief updating in this framework remains to be constantly changing, until the point of decision making is reached. During the dynamic change of beliefs, the intelligent agents will incorporate their knowledge about other agents, and the possible uncertain information they received from their local sensors and other agents.