The use of approximation as a method for dealing with complex problems is a fundamental research issue in Knowledge Representation. Using approximation in symbolic AI is not strai...
Although there are high expectations for collaborative discussion and on-line learning, existing systems for on-line discussion and chat facilities are not fully effective in prom...
The multiply sectioned Bayesian network (MSBN) framework is the most studied approach for distributed Bayesian Network inference in an MAS setting. This paper describes a new fram...
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve...
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e...
Mixed-initiative learning integrates complementary human and automated reasoning, taking advantage of their respective reasoning styles and computational strengths in order to sol...