1. John Anderson. Supporting Intelligent Agents in Individual-Based Ecosystem Models. In Proceedings of the Eleventh Annual Conference on Geographic Information Systems, Vancouver, BC, pages 3-6, February 1997.
   

   @inproceedings{gis97,
   author = {John Anderson},
   title = {Supporting Intelligent Agents in Individual-Based Ecosystem Models},
   booktitle = {Proceedings of the Eleventh Annual Conference on Geographic Information Systems},
   year = {1997},
   pages = {3-6},
   address = {Vancouver, BC},
   month = {February} 
   }
   




2. John Anderson. Waffler: A Constraint-Directed Approach to Intelligent Agent Design. In Proceedings of the AAAI-97 Workshop on Constraints and Agents, July 1997.
   

   @inproceedings{constragents,
   author = {John Anderson},
   title = {Waffler: A Constraint-Directed Approach to Intelligent Agent Design},
   booktitle = {Proceedings of the AAAI-97 Workshop on Constraints and Agents},
   year = {1997},
   month = {July} 
   }
   




3. Jacky Baltes. DoLittle: A Multi-strategy planning system. In M.H. Hamza, editor, Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, pages 435-439, July 1997. IASTED, IASTED Acta Press. Abstract:

   This paper introduces multi-strategy planning, which focuses on the selection and combination of different planning methods. Planning is the problem of finding a sequence of actions (operators) that will take an agent from one state (initial state) to a desired state (goal). This problem has gotten considerable attention in artificial intelligence. Unfortunately, theoretical results show that the general planning problem is intractable in complex domains. Therefore, a practical planning system reduce the search space. This reduction of the search space is based on assumptions (so called \emph{planning biases\/}) about the problem such as: the problem order, plan structure, or subgoal hierarchy. Given these assumptions about the task, a \emph{planning strategy\/} exploits the reduction in the search space and searches the resulting search space. Popular examples of planning strategies are means-ends analysis, case-based planning, macro-operators, abstraction hierarchies, and non-linear planning. Planning strategies based on a specific planning bias work well in domains, in which these assumptions are satisfied, but fail if these assumptions are not met. Furthermore, in complex domains it is possible that only parts of a task can be efficiently solved with a given planning method. But for other parts of the tasks, a different planning strategy may be appropriate.

   
   

   @inproceedings{baltes-1997,
   author = {Jacky Baltes},
   title = {DoLittle: A Multi-strategy planning system},
   booktitle = {Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing},
   editor = {M.H. Hamza},
   year = 1997,
   organization = {IASTED},
   publisher = {IASTED Acta Press},
   month = {July},
   pages = {435-439},
   abstract = {This paper introduces multi-strategy planning, which focuses on the selection and combination of different planning methods. Planning is the problem of finding a sequence of actions (operators) that will take an agent from one state (initial state) to a desired state (goal). This problem has gotten considerable attention in artificial intelligence. Unfortunately, theoretical results show that the general planning problem is intractable in complex domains. Therefore, a practical planning system reduce the search space. This reduction of the search space is based on assumptions (so called \emph{planning biases\/}) about the problem such as: the problem order, plan structure, or subgoal hierarchy. Given these assumptions about the task, a \emph{planning strategy\/} exploits the reduction in the search space and searches the resulting search space. Popular examples of planning strategies are means-ends analysis, case-based planning, macro-operators, abstraction hierarchies, and non-linear planning. Planning strategies based on a specific planning bias work well in domains, in which these assumptions are satisfied, but fail if these assumptions are not met. Furthermore, in complex domains it is possible that only parts of a task can be efficiently solved with a given planning method. But for other parts of the tasks, a different planning strategy may be appropriate.},
   pdf = {http://aalab.cs.umanitoba.ca/%7ejacky/Publications/pdf/baltes-1997.pdf} 
   }
   




4. Linda Strachan, John Anderson, Murray Sneesby, and Mark Evans. Pragmatic User Modelling in a Commercial Software System. In Proceedings of The Sixth International Conference on User Modelling, Sardinia, pages 189-200, June 1997.
   

   @inproceedings{um97,
   author = {Linda Strachan and John Anderson and Murray Sneesby and Mark Evans},
   title = {Pragmatic User Modelling in a Commercial Software System},
   booktitle = {Proceedings of The Sixth International Conference on User Modelling},
   year = {1997},
   pages = {189-200},
   address = {Sardinia},
   month = {June},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/PUM.pdf} 
   }
   

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