Abstract

In this thesis we investigate the relationships between the symbolic and sub-symbolic methods used for controlling agents by artificial intelligence, focusing in particular on methods that learn. In light of the strengths and weaknesses of each approach, we propose a hybridisation of symbolic and subsymbolic methods to capitalise on the best features of each. We implement such a hybrid system, called Rachel which incorporates techniques from Teleo-Reactive Planning, Hierarchical Reinforcement Learning and Inductive Logic Programming. Rachel uses a novel representation of be-haviours, Reinforcement-Learnt Teleo-operators (RL-Tops), which defines the behaviour in terms of its desired consequences but leaves the implementation of the policy to be learnt by reinforcement learning. An RL-Top is an abstract, symbolic description of the purpose of a behaviour, and is used by Rachel both as a planning operator and as the definition of a reward function by which the behaviour can be learnt. Two new

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