Action representation is fundamental to many aspects of cognition, including language.
Theories of situated cognition suggest that the form of such representation is distinctively
determined by grounding in the real world. This thesis tackles the question of
how to ground action representations, and proposes an approach for learning action
models in noisy, partially observable domains, using deictic representations and kernel
perceptrons.
Agents operating in real-world settings often require domain models to support
planning and decision-making. To operate effectively in the world, an agent must be
able to accurately predict when its actions will be successful, and what the effects of its
actions will be. Only when a reliable action model is acquired can the agent usefully
combine sequences of actions into plans, in order to achieve wider goals. However,
learning the dynamics of a domain can be a challenging problem: agents’ observations
may be noisy, or incomplete; actions may be non-deterministic; the world itself may
be noisy; or the world may contain many objects and relations which are irrelevant.
In this thesis, I first show that voted perceptrons, equipped with the DNF family
of kernels, easily learn action models in STRIPS domains, even when subject to noise
and partial observability. Key to the learning process is, firstly, the implicit exploration
of the space of conjunctions of possible fluents (the space of potential action preconditions)
enabled by the DNF kernels; secondly, the identification of objects playing
similar roles in different states, enabled by a simple deictic representation; and lastly,
the use of an attribute-value representation for world states.
Next, I extend the model to more complex domains by generalising both the kernel
and the deictic representation to a relational setting, where world states are represented
as graphs. Finally, I propose a method to extract STRIPS-like rules from the learnt
models. I give preliminary results for STRIPS domains and discuss how the method
can be extended to more complex domains. As such, the model is both appropriate for
learning data generated by robot explorations as well as suitable for use by automated
planning systems. This combination is essential for the development of autonomous
agents which can learn action models from their environment and use them to generate
successful plans