A goal directed learning agent for the Semantic Web
Gunnar Aastrand Grimnes
PhD-Thesis, University of Aberdeen, 2009.
This thesis is motivated by the need for autonomous agents on the Semantic Web to be able to
learn. The Semantic Web is an effort for extending the existing Web with machine understandable
information, thus enabling intelligent agents to understand the content of web-pages and help users
carrying out tasks online. For such autonomous personal agents working on a world wide Semantic
Web we make two observations: Firstly, every user is different and the Semantic Web will never
cater for them all - therefore, it is crucial for an agent to be able to learn from the user and the
world around it to provide a personalised view of the web. Secondly, due to the immense amounts
of information available on the world wide Semantic Web an agent cannot read and process all
available data. We argue that to deal with the information overload a goal-directed approach is
needed; an agent must be able to reason about the external world, the internal state and the actions
available and only carry out the actions that help achieve the current goal.
In the first part of this thesis we explore the application of two machine learning techniques
to Semantic Web data. Firstly, we investigate the classification of Semantic Web resources, we
discuss the issues of mapping Semantic Web format to an input representation suitable for a selec-
tion of well-known algorithms, and outline the requirements for these algorithms to work well in a
Semantic Web context. Secondly, we consider the clustering of Semantic Web resources. Here we
focus on the definition of the similarity between two resources, and how we can determine what
part of a large Semantic Web graph is relevant to a single resource.
In the second part of the thesis we describe our goal-directed learning agent Smeagol. We
present explicit definitions of the classification and clustering techniques devised in the first part of
the thesis, allowing Smeagol to use a planning approach to create plans of actions that may fulfil
a given top-level goal. We also investigate different ways that Smeagol can dynamically replan
when steps within the initial plan fail and show that Smeagol can offer plausible learned answers
to a given query, even when no explicit correct answer exists.