Bob Givan

465 Northwestern Avenue, Rm. 313C
Electrical Engineering Building
School of Electrical & Computer Engineering
Purdue University
West Lafayette, IN 47907

The following information is backwards:

8609-494 (567)    (office, backwards)
7248-235 (567)    (mobile, backwards)
ude.eudrup@navig    (email, backwards)

Title: Associate Professor
Program: Computer Engineering

To see me, send me email, or visit my Office Hours. If an email to me goes unanswered for a couple days, please repeat it.

My research is in several areas: machine planning, machine learning, reinforcement learning, knowledge representation, stochastic modeling, and automated reasoning. I am currently focusing on applying automated reasoning to AI planning domains. I have worked in the past applying AI techniques to the control of computer communications networks and processor branch prediction. I have also previously published in the areas of automated reasoning, formal methods, type inference, programming languages, and logic in computer science.

Publications

Congruence closure with ACI function symbols

with Tanji Hu

Automated Deduction: Decidability, Complexity, Tractability (ADDCT 2013) Affiliated with CADE-24 (Conference on Automated Deduction) Lake Placid, New York June 9 - 14, 2013

Formal Verification and Planning: An Evaluation

with Rajesh Kalyanam and Tanji Hu

Purdue ECE Technical Report TR-ECE-12-12, October, 2013

Reasoning About Planning Domains

with Rajesh Kalyanam and Tanji Hu

AAAI 2011 Workshop on Generalized Planning

Stochastic Enforced Hill-Climbing

with Jia-Hong Wu and Rajesh Kalyanam

JAIR 42:815-850, 2011

PDF, online appendix 1 (pdf), online appendix 2 (zip), online appendix 3 (pdf)

Automatic Induction of Bellman-Error Features for Probabilistic Planning

with Jia-Hong Wu

JAIR 38:687-755, 2010

PDF, online appendix

Stochastic Enforced Hill-Climbing

with Jia-Hong Wu and Rajesh Kalyanam

ICAPS '08

Probabilistic Planning via Determinization in Hindsight

with Sungwook Yoon, Alan Fern, and Subbarao Kambhampati

AAAI '08

FF-Replan: A Baseline for Probabilistic Planning

with Sungwook Yoon and Alan Fern

ICAPS '07

Using Learned Policies in Heuristic-Search Planning

with Sungwook Yoon and Alan Fern

IJCAI '07

pdf on request

Discovering relational domain features for probabilistic planning

with Jia-Hong Wu

ICAPS 2007

Approximate Policy Iteration with a Policy Language Bias: Learning to Solve Relational Markov Decision Processes

with Alan Fern and Sungwook Yoon

JAIR 25:85-118, 2006

Learning Heuristic Functions from Relaxed Plans

with Sungwook Yoon and Alan Fern

ICAPS '06

pdf on request

Learning Measures of Progresss for Planning Domains

with Sungwook Yoon and Alan Fern

AAAI '05

pdf on request

Feature-discovering Approximate Value Iteration Methods

with Jia-Hong Wu

SARA 2005

Simultaneous Heuristic Search for Conjunctive Subgoals

with Lin Zhu

AAAI 2005

Feature-discovering Approximate Value Iteration Methods

with Jia-Hong Wu

Purdue ECE Technical Report TR-ECE-04-06, November, 2004

Approximation Results on Sampling Techniques for Zero-sum, Discounted Markov Games

with Uday Savagaonkar and Edwin K. P. Chong

draft

Dynamic Feature Selection for Hardware Prediction

with Alan Fern, Babak Falsafi, and T. N. Vijaykumar

JSA 2005

Relational Sequential Inference with Reliable Observations

with Alan Fern

ICML 2004

Parallel Rollout for Online Solution of Partially Observable Markov Decision Processes

with Hyeong Soo Chang and Edwin K. P. Chong

Discrete Event Dynamic Systems, 2004

Learning Domain-Specific Control Knowledge from Random Walks

with Alan Fern and SungWook Yoon

ICAPS-04

Approximate Policy Iteration with a Policy Language Bias

with Alan Fern and SungWook Yoon

NIPS-2003

Online Pricing for Bandwidth Provisioning in Multi-class Networks

with Uday Savagaonkar and Edwin K. P. Chong

Computer Networks, April 2004

Online Ensemble Learning: An Empirical Study

with Alan Fern

Machine Learning, October 2003

Equivalence Notions and Model Minimization in Markov Decision Processes

with Tom Dean and Matt Greig

Artificial Intelligence, July 2003

Prediction-based Video Streaming

with Gang Wu and Edwin K. P. Chong

IEEE 2003 Global Communications Conference (GLOBECOM)

Congestion Control using Policy Rollout

with Gang Wu and Edwin K. P. Chong

IEEE 2003 Conference on Decision and Control

The Complexity of Decentralized Control of Markov Decision Processes

with Daniel Bernstein, Neil Immerman, and Shlomo Zilberstein

Mathematics of Operations Research, November, 2002

Streaming stored video over AIMD transport protocols

with Gang Wu and Edwin K. P. Chong

2002 IEEE International Symposium on Multimedia Software Engineering

Sampling Techniques for Zero-sum, Discounted Markov Games

with Uday Savagaonkar and Edwin K. P. Chong

Allerton Conference on Control and Communications, 2002

Specific-to-General Learning for Temporal Events with Application to Video Event Recognition

with Alan Fern and Jeffrey Mark Siskind

Journal of Artificial Intelligence Research (JAIR), December, 2002

Inductive Policy Selection for First-Order Markov Decision Processes

with SungWook Yoon and Alan Fern

UAI-02

Specific-to-General Learning for Temporal Events

with Alan Fern and Jeffrey Mark Siskind

AAAI-02

Learning Temporal, Relational, Force-Dynamic Event Definitions from Video

with Alan Fern and Jeffrey Mark Siskind

AAAI-02

Scheduling Multiclass Packet Streams to Minimize Weighted Loss

with Hyeong Soo Chang and Edwin K. P. Chong

Queueing Systems (QUESTA), July 2002

Burst-level Congestion Control using Hindsight Optimization

with Gang Wu and Edwin K. P. Chong

IEEE Transactions on Automatic Control, June, 2002

Tarskian Set Constraints

with David McAllester, Carl Witty, and Dexter Kozen

Information and Computation, May, 2002

Dynamic Pricing for Bandwidth Provisioning

with Uday Savagaonkar and Edwin K. P. Chong

Conference on Information Sciences and Systems (CISS-02), 2002

Polynomial-time Computation via Local Inference Relations

with David McAllester

ACM Transactions on Computational Logic, October, 2002

Congestion Control via Online Sampling

with Gang Wu and Edwin K. P. Chong

Infocom 2001

A Framework for Simulation-based Network Control via Hindsight Optimization

with Edwin K. P. Chong and Hyeong Soo Chang

IEEE 2000 Conference on Decision and Control

Online Ensemble Learning: An Empirical Study

with Alan Fern

International Conference on Machine Learning, 2000 (ICML-2000)

Online Scheduling via Sampling

with Hyeong Soo Chang and Edwin K. P. Chong

Artificial Intelligence Planning and Scheduling, 2000 (AIPS-2000)

Bounded Parameter Markov Decision Processes

with Tom Dean and Sonia Leach

Artificial Intelligence, 2000

Solving Stochastic Planning Problems with Large State and Action Spaces

with Kee-Eung Kim and Tom Dean

AIPS-98

Obvious Properties of Computer Programs

AAAI-97

Model Minimization in Markov Decision Processes

with Tom Dean

AAAI-97

Model Minimization, Regression and Propositional STRIPS Planning

with Tom Dean

IJCAI-97

Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes

with Tom Dean and Sonia Leach

Uncertainty in AI, 1997

Bounded Parameter Markov Decision Processes

with Tom Dean and Sonia Leach

European Conference on Planning, 1997

Inferring Program Specifications in Polynomial-time

Static Analysis Symposium, 1996

Tarskian Set Constraints

with David McAllester, Carl Witty, and Dexter Kozen

Logic in Computer Science, 1996

Taxonomic Syntax for First Order Inference

with David McAllester

Journal of the ACM, 1993

Natural Language Syntax and First Order Inference

with David McAllester

Artificial Intelligence, 1992

New Results on Local Inference Relations

with David McAllester

Conference on Knowledge Representation, 1992

Natural Language Based Inference Procedures Applied to Schubert's Steamroller

with David McAllester and Shameer Shalaby

AAAI-91

Automatically Inferring Properties of Computer Programs

MIT PhD Thesis, June 1996

Some Talk Slides

Knowledge Representation Meets Stochastic Planning , Dagstuhl, May, 2003.

Sampling Techniques for Large Markov Decision Processes, Dagstuhl, November, 2001.

MDP Tutorial --- definitions and extensions, Dagstuhl, November, 2001.

Large State Space Techniques for Markov Decision Processes, Dagstuhl, November, 2001.

Acknowledgement: much of the research reported above was supported by NSF grants 9977981-IIS, 0093100-IIS, 0905372-IIS, and 0098089-ECS.
Purdue University is an equal access/equal opportunity university. If you have trouble accessing this page because of a disability, please contact Bob Givan using the email address formed by his last name and purdue.edu