Abstract. One of the main questions concerning learning in a Multi-Agent System's environment is: "(How) can agents benefit from mutual interaction during the learning pr...
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 adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents operating in multi-agent environments. We use the...
Large-scale distributed environments, such as the Internet, achieve advantages in exploiting software agents for applications, thanks to their autonomy in carrying out tasks. In s...
In this paper we present an extension of logic programming (LP) that is suitable not only for the "rational" component of a single agent but also for the "reactive&...