Scott C. Proper

*** ** ****, ***. *

Corvallis, OR 97330

phone: 541-***-****

e-mail: abqdb4@r.postjobfree.com

homepage: http://web.engr.oregonstate.edu/~proper/

Objective

To contribute to the computer science community via research in artificial intelligence.

Specifically, I plan to finish my PhD and go on to a career in research, continuing work in reinforcement learning.

I am currently seeking a post-doc position or an industry research position.

Education

Montana State University, Bozeman

Oregon State University

Expected graduation date: Fall 2009

Computer related skills

Programming languages: C/C++, BASIC, Lisp, Assembly, Perl, SQL

Platforms: Linux, Windows, UNIX

Web Skills and languages: HTML, Java, CGIRelevant courseworkSoftware Engineering, Programming Language DesignTheory of Computation, Operating Systems, CompilersComputer Architecture, Computer NetworksComputer Graphics, Image Processing, DatabasesArtificial Intelligence, Reinforcement Learning, CyberneticsAlgorithms and Data Structures, Bayesian Networks, Graph TheoryWork HistoryWebmaster, Jet Propulsion Laboratories, May 1999-December 1999.

Primary designer of a web page for engineers, served across the JPL intranet.

Programmer, Dyonjet Research, 2001

Primary programmer of numerous in-house utilities. Designed GUI software for release to the public.

Research Assistant, Oregon State University, 2002-Present

Current work includes research on scaling reinforcement learning, particularly in multiagent domains.

Research Contributions Developed several techniques to mitigate the "three curses of dimensionality" (explosions in state, action, and stochastic

branching factor) of reinforcement learning problems,

including a new kind of function approximation that generalizes both tables and linear functions -- "Tabular Linear Functions" --

and the creation of a new average-reward model-based value iteration algorithm based on afterstates, "ASH-learning".

Developed a new technique -- "Assignment-based Decomposition" -- for decomposing states and actions in multi-agent,

multi-task domains that

greatly mitigates the three curses of dimensionality by dividing the action-selection step of a reinforcement learning

algorithm into two stages: an upper assignment level and a lower task performance level.

Further developed and expanded upon assignment-based decomposition by showing how to integrate coordination

graphs into the lower task performance level, how to use it together with transfer learning to enable multi-agent domains to scale

to large numbers of agents, and how to use search techniques to quickly assign agents to appropriate tasks.

PublicationsProper, S., Tadepalli, P., Tang, H., Logendran, R.,

A Reinforcement Learning Approach for Product Delivery by Multiple Vehicles,

for IIE/IERC 2003: Institute of Industrial Engineers/Industrial Engineering Research Conference.

Proper, S., Tadepalli, P.,

Scaling Average-reward Reinforcement Learning for Product Delivery,

for AAAI Real Life Reinforcement Learning Fall Symposium 2004.

Proper, S., Tadepalli, P.,

Scaling Model-Based Average-reward Reinforcement Learning for Product Delivery,

in ECML 2006: Proceedings of the 17th European Conference on Machine Learning, p 735-742.

Proper, S., Tadepalli, P.,

Solving Multiagent Assignment Markov Decision Processes,

in AAMAS 2009: Proceedings of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems., p 681-688

Proper, S., Tadepalli, P.,

Transfer Learning via Relational Templates,

in ILP 2009: Proceedings of the 19th International Joint Conference on Inductive Logic Programming. (to appear)

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