A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its c...
Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In thi...
Kennard Laviers, Gita Sukthankar, David W. Aha, Ma...
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 game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process t...
Robert Speer, Jayant Krishnamurthy, Catherine Hava...
Although well understood in the single-agent framework, the use of traditional reinforcement learning (RL) algorithms in multi-agent systems (MAS) is not always justified. The fe...