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b. 1950. Ph.D. (1979) University of Toronto. Assistant Professor (79-84), Associate Professor (84-87), Department Chair (87-90), Professor (87-present), Dessauer Chair (92-present); University of Rochester. Editor-in-Chief, Computational Linguistics (83-93; Presidential Young Investigator (84-89); author of Natural Language Understanding, Benjamin Cummings (87), 2nd edition (1995); Reasoning About Plans, Morgan Kaufmann (91); co-editor of Readings in Planning, Morgan Kaufmann (90); Fellow of the AAAI.
James Allen's research interests span a range of issues covering natural language understanding, discourse, knowledge representation, common-sense reasoning and planning. These areas of research are combined in the TRAINS project, a long term effort co-directed with Len Schubert. The TRAINS system is an intelligent planning assistant that can converse in spoken natural language with a person to create, discuss and evaluate various plans involving freight shipments by train. In particular, Allen's research breaks down into two main subareas, broadly classified as research in discourse and research in plan reasoning.
The research in discourse is focused on two-person extended dialogs in which the speakers have specific tasks to accomplish. An emphasis in this work is the representation and use of the context of the dialog to solve problems in semantic interpretations and the recognition of the intentions underlying the speakers' utterances. Work in this area has included developing the first computational model of speech acts, the development of a multi-level plan-based analysis involving discourse-level plans as well as domain-level plans, and the development of several different discourse-plan recognition algorithms. In addition, we are exploring how prosody and intonation signals discourse intentions and how this interacts with the plan-based dialog model. While it is important for work to be formally well-defined and understood, it is equally important that computational theories can lead to effective implementations. As a result, a considerable amount of effort has also been made in developing an expressive hybrid knowledge representation system that can support complex reasoning about plans and actions.
The research in plan reasoning draws much of its motivation from the dialog work. In particular, the representation of plans must support a wide range of different forms of reasoning: plan construction (i.e. traditional planning), plan recognition, plan evaluation, and the communication of plans between agents. Much of our work in this area has focused on the representation of time and action, and we have reformulated the planning problem as a problem in temporal reasoning. Within this framework, we have developed a representation of plans that is temporally explicit and supports plan construction, recognition and communication. We are also exploring methods of temporal reasoning that are viable even with large data sets of temporal information.
The second is the parser from the TRAINS system, which is also a bottom-up chart parser that closely follows Natural Language Understanding, Second Edition, but is written more for efficiency and has additional capabilities that help construct larger scale grammars. The Users Manual gives more information on this system and instructions on how to obtain it.
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