Browsing by Subject "agents"
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Item Intention is commitment with expectation(Texas A&M University, 2005-08-29) Creel, James SilasModal logics with possible worlds semantics can be used to represent mental states such as belief, goal, and intention, allowing one to formally describe the rational behavior of agents. Agent??s beliefs and goals are typically represented in these logics by primitive modal operators. However, the representation of agent??s intentions varies greatly between theories. Some logics characterize intention as a primitive operator, while others define intention in terms of more primitive constructs. Taking the latter approach is a theory due to Philip Cohen and Hector Levesque, under which intentions are a special form of commitment or persistent goal. The theory has motivated theories of speech acts and joint intention and innovative applications in multiagent systems and industrial robotics. However, Munindar Singh shows the theory to have certain logical inconsistencies and permit certain absurd scenarios. This thesis presents a modification of the theory that preserves the desirable aspects of the original while addressing the criticism of Singh. This is achieved by the introduction of an additional operator describing the achievement of expectations, refined assumptions, and new defi- nitions of intention. The modified theory gives a cogent account of the rational balance between agents?? action and deliberation, and suggests the use of meansends reasoning in agent implementations. A rule-based reasoner in Jess facilitates evaluation of the predictiveness and intuitiveness of the theory, and provides a prototypical agent based on the theory.Item Scaling reinforcement learning to the unconstrained multi-agent domain(2009-06-02) Palmer, VictorReinforcement learning is a machine learning technique designed to mimic the way animals learn by receiving rewards and punishment. It is designed to train intelligent agents when very little is known about the agent?s environment, and consequently the agent?s designer is unable to hand-craft an appropriate policy. Using reinforcement learning, the agent?s designer can merely give reward to the agent when it does something right, and the algorithm will craft an appropriate policy automatically. In many situations it is desirable to use this technique to train systems of agents (for example, to train robots to play RoboCup soccer in a coordinated fashion). Unfortunately, several significant computational issues occur when using this technique to train systems of agents. This dissertation introduces a suite of techniques that overcome many of these difficulties in various common situations. First, we show how multi-agent reinforcement learning can be made more tractable by forming coalitions out of the agents, and training each coalition separately. Coalitions are formed by using information-theoretic techniques, and we find that by using a coalition-based approach, the computational complexity of reinforcement-learning can be made linear in the total system agent count. Next we look at ways to integrate domain knowledge into the reinforcement learning process, and how this can signifi-cantly improve the policy quality in multi-agent situations. Specifically, we find that integrating domain knowledge into a reinforcement learning process can overcome training data deficiencies and allow the learner to converge to acceptable solutions when lack of training data would have prevented such convergence without domain knowledge. We then show how to train policies over continuous action spaces, which can reduce problem complexity for domains that require continuous action spaces (analog controllers) by eliminating the need to finely discretize the action space. Finally, we look at ways to perform reinforcement learning on modern GPUs and show how by doing this we can tackle significantly larger problems. We find that by offloading some of the RL computation to the GPU, we can achieve almost a 4.5 speedup factor in the total training process.