1998 AMERICAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE FALL SYMPOSIUM PLANNING WITH PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES Working notes Table of Contents BAGNELL Drew Bagnell 1- 6 Comparison of Reinforcement Learning Techniques for Automatic Behavior Programming by J. A. Bagnell, K. L. Doty, A. A. Arroyo BAYER Valentina Bayer 7- 8 Cost-observable MDPs by V. Bayer BONET Blai Bonet (see H. Geffner) no paper BOUTILIER Craig Boutilier 9- 16 Computing Optimal Policies for Partially Observable Decision Processes using Compact Representations by C. Boutilier, D. Poole CASSANDRA Tony Cassandra 17- 24 A Survey of POMDP Applications by A. R. Cassandra CHAWLA Jay Chawla 25- 38 Risk-sensitive Optimal Control of Hidden Markov Models: Structural Results by E. Fernandez-Gaucherand, S. I. Marcus DARRELL Trevor Darrell 39- 56 Reinforcement Learning of Active Recognition Behaviors by T. Darrell ECKERMAN Dave Eckerman (see S. Kemp) no paper ENGELBRECHT Sascha E. Engelbrecht 57- 60 Planning with Delayed State Information by S. E. Engelbrecht, K. V. Katsikopoulos GEFFNER Hector Geffner 61- 68 Solving Large POMDPs using Real Time Dynamic Programming by H. Geffner, B. Bonet GOLDSMITH Judy Goldsmith 69- 78 Complexity Issues in Markov Decision Processes by J. Goldsmith, M. Mundhenk HANSEN Eric Hansen 79- 88 Solving POMDPs by Searching in Policy Space by E. Hansen HAUSKRECHT Milos Hauskrecht 89- 90 Statement of Interest by M. Hauskrecht KEMP Steve Kemp no paper LANZI Pier Luca Lanzi 91- 98 Adding Memory to Wilson's XCS Classifier System to Learn in Partially Observable Environments by P. L. Lanzi LI Tong Li (see J. Goldsmith) no paper LITTMAN Michael Littman no paper LUSENA Chris Lusena 99-110 Nonapproximability Results for Markov Decision Processes by C. Lusena, J. Goldsmith, M. Mundhenk MADANI Omid Madani 111-112 Abstract by O. Madani MAHADEVAN Sridhar Mahadevan 113-120 Partially Observable Semi-Markov Decision Processes: Theory and Applications in Engineering and Cognitive Science by S. Mahadevan MAJERCIK Steve Majercik 121-128 MAXPLAN: A New Approach to Probabilistic Planning by S. M. Majercik, M. L. Littman PEEK Niels Peek 129-134 Predictive Probabilistic Models for Treatment Planning in Paediatric Cardiology by N. Peek PLATZMAN Loren Platzman no paper PYEATT Larry Pyeatt no paper ROY Nicholas Roy 135-140 Coastal Navigation -- Robot Motion with Uncertainty by N. Roy, W. Burgard, D. Fox, S. Thrun SINGH Satinder Singh 141-150 Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes by J. Loch, S. Singh SITTINGER Shelia Sittinger (see J. Goldsmith) no paper TOUZET Claude Touzet 151-152 Cooperative Reinforcement Learning and Hidden Markov Models by C. Touzet WASHINGTON Rich Washington 153-164 BI-POMDP: Bounded, Incremental Partially-Observable Markov-Model Planning by R. Washington WELLS Chris Wells (see J. Goldsmith) no paper WHITE Chip White 165-168 Nonhomogeneous Markov Decision Processes by J. C. Bean, C. C. White, III, Z-Z. Lin ZHANG Nevin Zhang 169-176 Planning with Partially Observable Markov Decision Processes: Advanced in Exact Solution Method by N. L. Zhang, S. S. Lee ZHANG Weihong Zhang (see N. Zhang) no paper