This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...
Nowadays standard technologies play important roles in enhancing sharability, reusability and interoperability of learning contents. However, there is a lack of pedagogical justifi...
This paper presents an integrated modeling framework where the learning and knowledge retrieval mechanisms of the ACT-R cognitive architecture are combined with a semantic resource...
It is possible to model avatars that learn to simulate object manipulations and other complex actions. A number of applications may benefit from this technique including safety, e...
This paper presents and evaluates sequential instance-based learning (SIBL), an approach to action selection based upon data gleaned from prior problem solving experiences. SIBL le...