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 ...
We present a framework for incorporating perception-induced beliefs into the knowledge base of a rational agent. Normally, the agent accepts the propositional content of perception...
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...
We present a practical framework for registering a Mixed Reality(MR) environment of an arbitrary number of agents. Each agent consist of a head mounted display (HMD), which consis...
The use of rational agents for modelling real world problems has both been heavily investigated and become well accepted, with BDI Logic being a widely used architecture to repres...
Jeff Blee, David Billington, Guido Governatori, Ab...