Dialogue systems for virtual environments
YIWCALA '10 Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas
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This thesis describes research which attempts to remove some of the barriers to creating true conversational agents---autonomous agents which can communicate with humans in natural language. First, in order to help bridge the gap between research in the natural language and agents communities, we define a model of agent-agent collaborative problem solving which formalizes agent communication at the granularity of human communication. We then augment the model to define an agent-based model of dialogue, which is able to describe a much wider range of dialogue phenomena than plan-based models. The model also defines a declarative representation of communicative intentions for individual utterances. Recognition of these intentions from utterances will require an augmentation of already intractable plan and intention recognition algorithms. The second half of the thesis describes research in applying statistical corpus-based methods to goal recognition, a special case of plan recognition. Because of the paucity of data in the plan recognition community, we have generated two corpora in distinct domains. We also define an algorithm which can stochastically generate artificial corpora to be used in learning. We then describe and evaluate fast statistical algorithms for both flat and hierarchical recognition of goal schemas and their parameter values. The recognition algorithms are more scalable than previous work and are able to recognize goal parameter values as well as schemas.