Oates, Tim; Schmill, Matthew D.; and
Cohen, Paul R. A Method for Clustering the
Experiences of a Mobile Robot that Accords with Human Judgments. In
Proceedings of the Seventeenth National Conference on Artificial
Intelligence, pages 846-851, 2000.
Heeringa, Brent; and Oates, Tim. Incrementally Learning the Parameters of
Stochastic Context-Free Grammars Using Summary Statistics. Under
review.
Oates, Tim; Prasad,
M. V. Nagendra; and Lesser, Victor R. Cooperative Information Gathering: a
Distributed Problem Solving Approach. In IEE/BCS Journal of
Software Engineering special issue on Developing Agent Based
Systems, 144(1):72-88, 1997.
Oates, Tim; Schmill, Matthew D.; and
Cohen, Paul R. A Method for Clustering the
Experiences of a Mobile Robot that Accords with Human Judgments. In
Proceedings of the Seventeenth National Conference on Artificial
Intelligence, pages 846-851, 2000.
Schmill, Matthew D.; Oates, Tim; and
Cohen, Paul R. Learning Planning Operators
in Real-World, Partially Observable Environments. In Proceedings of
the Fifth International Conference on Artificial Intelligence Planning and
Scheduling, pages 246-253, 2000.
Oates, Tim; Eyler-Walker, Zachary.; and
Cohen, Paul R. Toward Natural Language
Interfaces for Robotic Agents: Grounding Linguistic Meaning in Sensors.
In Proceedings of the Fourth International Conference on Autonomous
Agents, pages 227-228, 2000. Extended abstract.
Oates, Tim; Schmill, Matthew D.; and
Cohen, Paul R. Identifying Qualitatively
Different Outcomes of Actions: Gaining Autonomy Through Learning. In
Proceedings of the Fourth International Conference on Autonomous
Agents, pages 110-111, 2000. Extended abstract.
Oates, Tim. Identifying Distinctive Subsequences in
Multivariate Time Series by Clustering. In in Proceedings of the
Fifth International Conference on Knowledge Discovery and Data Mining,
pages 322-326, 1999. Long
version.
Provost, Foster; Jensen, David; and
Oates, Tim. Efficient Progressive
Sampling. In Proceedings of the Fifth International Conference on
Knowledge Discovery and Data Mining, pages 23-32, 1999.
Oates, Tim and Jensen, David.
Toward a Theoretical Understanding
of Why and When Decision Tree Pruning Algorithms Fail. In
Proceedings of the Sixteenth National Conference on Artificial
Intelligence, pages 372 - 378, 1999.
Oates, Tim; Schmill, Matthew D.; and
Cohen, Paul R. Efficient Mining of
Statistical Dependencies. In Proceedings of the Sixteenth
International Joint Conference on Artificial Intelligence, pages
794-799, 1999.
Oates, Tim and Jensen, David.
Large Datasets Lead to Overly
Complex Models: an Explanation and a Solution. In Proceedings
of the Fourth International Conference on Knowledge Discovery and Data
Mining, pages 294 - 298, 1998.
Firoiu, Laura; Oates, Tim; and
Cohen, Paul R. Learning
Regular Languages from Positive Evidence. In Proceedings of
the Twentieth Annual Meeting of the Cognitive Science Society,
pages 350 - 355, 1998.
Oates, Tim and Jensen, David.
The Effects of Training Set Size on
Decision Tree Complexity. In Proceedings of The Fourteenth
International Conference on Machine Learning, pages 254 - 262,
1997.
Oates, Tim; Schmill, Matthew D.;
and Cohen, Paul R. Parallel and
Distributed Search for Structure in Multivariate Time Series. In
Proceedings of the Ninth European Conference on Machine
Learning, 1997.
Cohen, Paul R; Atkin, Marc S.;
Oates, Tim; and Beal, Carole R. Neo: Learning Conceptual Knowledge
by Sensorimotor Interaction with an Environment. In Proceedings
of the First International Conference on Autonomous Agents, pages
170 - 177, 1997.
Cohen, Paul R; Oates, Tim; Atkin,
Marc S.; and Beal, Carole R. Building a Baby. In
Proceedings of the Eighteenth Annual Conference of the Cognitive
Science Society, pages 518 - 522, 1996.
Oates, Tim and Cohen, Paul R.
Searching for Structure in Multiple
Streams of Data. In Proceedings of the Thirteenth International
Conference on Machine Learning, pages 346 - 354, 1996.
Oates, Tim and Cohen, Paul R.
Searching for Planning Operators
with Context-Dependent and Probabilistic Effects. In
Proceedings of the Thirteenth National Conference on Artificial
Intelligence, pages 863 - 868, 1996.
Schmill, Matthew D.; Oates, Tim;
and Cohen, Paul R. Tools for
Detecting Dependencies in AI Systems. In Proceedings of the
Seventh International IEEE Conference on Tools with Artificial
Intelligence, pages 148 - 155, February 1995.
Oates, Tim and Cohen, Paul R.
Toward
a Plan Steering Agent: Experiments with Schedule Maintenance. In
Proceedings of the Second International Conference on AI Planning
Systems, pages 134 - 139. AAAI Press, 1994.
Provost, Foster; Jensen, David; and
Oates, Tim. Progressive Sampling. In H. Liu and H. Motoda, editors,
Instance Selection and Construction, A Data Mining Perspective.
Kluwer, 2001. Also appeared in proceedings of KDD-99.
Oates, Tim; Firoiu, Laura; and Cohen,
Paul R. Using Dynamic Time Warping
to Bootstrap HMM-Based Clustering of Time Series. In Ron Sun and Lee
Giles, editors, Sequence Learning: Paradigms, Algorithms and
Applications. Springer-Verlag, 2000.
Cohen, Paul R.; Oates, Tim; and
St. Amant, Robert. Plan
Steering and Mixed-Initiative Planning. In Austin Tate, editor,
ARPI Supplement to Proceedings of the Third International
Conference on Artificial Intelligence Planning Systems, pages 105
- 112. AAAI Press, 1996.
Oates, Tim; Schmill, Matthew D.;
Gregory, Dawn E.; and Cohen, Paul R. Detecting Complex Dependencies in
Categorical Data. In D. Fisher and H. Lenz, editors, Learning
from Data: Artificial Intelligence and Statistics, pages 185 -
195. Springer Verlag, 1995. Long version of workshop paper with same
title.
Oates, Tim; Schmill, Matthew D.;
and Cohen, Paul R. Identifying Qualitatively
Different Outcomes of Actions: Experiments with a Mobile Robot.
In Working Notes of the IJCAI-99 Workshop on Robot Action
Planning, pages 30-36, 1999. Long version of paper that appeared in
Agents-00.
Oates, Tim; Firoiu, Laura; and Cohen,
Paul R. Clustering Time Series with
Hidden Markov Models and Dynamic Time Warping. In Proceedings of the
IJCAI-99 Workshop on Neural, Symbolic and Reinforcement Learning Methods
for Sequence Learning, pages 17-21, 1999. Abbreviated version of book
chapter by Oates, Firoiu and Cohen.
Schmill, Matthew D.; Oates, Tim;
and Cohen, Paul R. Learned
Models for Continuous Planning. In Preliminary Papers of the
Seventh International Workshop on Artificial Intelligence and
Statistics, pages 272 - 282, 1998.
Oates, Tim; Cohen, Paul R.; and
Durfee, Casey. Efficient
Mining of Statistical Dependencies. In Preliminary Papers of
the Seventh International Workshop on Artificial Intelligence and
Statistics, pages 133 - 141, 1998.
Firoiu, Laura; Oates, Tim; and
Cohen, Paul R. Learning a
Deterministic Finite Automaton with a Recurrent Neural Network. In
Proceedings of the Fourth International Colloquim on Grammatical
Inference, pages 90 - 101, 1998.
Oates, Tim; Jensen, David; and
Cohen, Paul R. Correlating and Predicting Asynchronous Events. In
Working Notes of the AAAI Workshop on Predicting the Future: AI
Approaches to Time Series Analysis, pages 73 - 79, 1998.
Cohen, Paul R. and Oates, Tim.
A Dynamical Basis for the Semantic Content of Verbs. In Working
Notes of the AAAI Workshop on The Grounding of Word Meaning: Data and
Models, pages 5 - 8, 1998.
Jensen, David; Oates, Tim and
Cohen, Paul R. Building Simple Models: A Case Study with Decision
Trees. In Proceedings of the Second International Symposium on
Intelligent Data Analysis, pages 211 - 222, 1997.
Oates, Tim; Schmill, Matthew D.;
Jensen, David; and Cohen, Paul R. A Family of Algorithms for
Finding Temporal Structure in Data. In The Preliminary Papers of
the Sixth International Workshop on Artificial Intelligence and
Statistics, pages 371 - 378, 1997.
Oates, Tim and Jensen, David.
The Effects of Training Set Size on Decision Tree Complexity. In
The Preliminary Papers of the Sixth International Workshop on
Artificial Intelligence and Statistics, pages 379 - 390, 1997. Also
appeared in proceedings of ICML-97.
Cohen, Paul R.; Oates, Tim; and
Atkin, Marc S. Preliminary Evidence that Conceptual Structure can
be Learned by Interacting with an Environment. In Working Notes of
the AAAI Fall Symposium on Embodied Cognition and Action, 1996.
Oates, Tim and Cohen, Paul R.
Learning Planning Operators with
Conditional and Probabilistic Effects. In Working Notes of the
AAAI Spring Symposium on Planning with Incomplete Information for
Robot Problems, pages 86 - 94, 1996. Also appeared in proceedings of
AAAI-96.
Cohen, Paul R. and Oates, Tim.
Finding Structure in Streams. In Advances in Intelligent Data
Analysis. Proceedings of the IDA-95 Symposium of the International
Institute for Systems Research, Informatics and Cybernetics, pages
27 - 31, 1995.
Technical Report 95-63, Dept. of Computer Science, University of
Massachusetts/Amherst.
Cohen, Paul R.; Atkin, Marc S.;
Oates, Tim; and Gregory, Dawn E. A Representation and Learning
Mechanisms for Mental States. In Working Notes of the Symposium on
Representing Mental States and Mechanisms, AAAI-95 Spring
Symposium Series.
Technical Report 95-53, Dept. of Computer Science, University of
Massachusetts/Amherst.
Oates, Tim; Prasad,
M. V. Nagendra; Lesser, Victor R.; and Decker, Keith. A Distributed Problem Solving Approach
to Cooperative Information Gathering. In Proceedings of the
AAAI Spring Symposium on Information Gathering from Heterogeneous,
Distributed Environments. 1995.
Oates, Tim; Prasad,
M. V. Nagendra; and Lesser, Victor R. Networked Information Retrieval as
Distributed Problem Solving. In Proceedings of the CIKM-94
Workshop on Intelligent Information Agents, 1994.
Oates, Tim; Gregory, Dawn E.; and
Cohen, Paul R. Detecting Complex
Dependencies in Categorical Data. In Preliminary Papers of the
Fifth International Workshop on Artificial Intelligence and
Statistics, 1995, pp. 417-423.
Oates, Tim and Cohen, Paul R.
Evaluation of a Mixed-Initiative
Approach to Schedule Maintenance. In Proceedings of the
Workshop on AI Approaches to Modelling and Scheduling Manufacturing
Processes, 1994 IEEE Conference on Tools with Artificial
Intelligence.
Oates, Tim and Cohen, Paul R.
Mixed-Initiative Schedule Maintenance: A First Step Toward Plan
Steering. In Proceedings, ARPA/Rome Lab Planning Initiative
Workshop, Tucson, AZ, 1994. Also Computer Science Department Technical
Report 94-31, University of Massachusetts, 1994.
Oates, Tim; Eyler-Walker, Zachary;
and Cohen, Paul R.. Using Syntax to
Learn Semantics: An Experiment in Language Acquisition with a Mobile
Robot. Computer Science Department Technical Report 99-35,
University of Massachusetts, Amherst, 1999. Long version of paper that
appeared in Agents-00.
Oates, Tim; Jensen, David; and
Cohen, Paul R. Automatically Acquiring Rules for Event Correlation
from Event Logs. Computer Science Department Technical Report
97-14, University of Massachusetts, Amherst, 1997.
Jensen, David; Oates, Tim; and
Cohen, Paul R. Prototype for ECS Fault Identification:
Requirements and Design. Report Prepared for Hughes Information
Technology Corporation, February 1996.
Oates, Tim; Jensen, David; and
Cohen, Paul R. Technologies for Fault Management. Report Prepared
for Hughes Information Technology Corporation, December 1995.
Oates, Tim; Jensen, David; and
Cohen, Paul R. Artificial Intelligence Technologies for Fault
Management: An Assessment. 1996.
Oates, Tim; Schmill, Matthew D.;
and Cohen, Paul R. Parallel and Distributed Search for Structure
in Multivariate Time Series. Computer Science Department Technical
Report 96-23, University of Massachusetts, Amherst, 1996.
Oates, Tim. Fault
Identification in Computer Networks: A Review and a New Approach.
Computer Science Department Technical
Report 95-113, University of Massachusetts, Amherst, 1995.
Oates, Tim. MSDD as a Tool for
Classification.Memo 94-29, Experimental Knowledge Systems
Laboratory, Department of Computer Science, University of
Massachusetts, Amherst, 1994.
Oates, Tim; Prasad, M. V. Nagendra;
and Lesser, Victor. Cooperative Information Gathering: a Distributed
Problem Solving Approach. Computer Science Department Technical
Report 94-66, University of Massachusetts, Amherst, 1994. Long
version of journal article.
Oates, Tim and Cohen, Paul R.
Humans Plus Agents Maintain Schedules Better than Either Alone.
Computer Science Department Technical
Report 94-03, University of Massachusetts, Amherst, 1994.