Carnegie Mellon University
 Computer Science Department

AI Seminar 01/02 Schedule

Time: Tuesday 3:30-4:30pm
Place: Wean Hall 5409

Organizer: Dr. Tuomas Sandholm (assistant: Phyllis Pomerantz

Speakers' Guide | Mailing List | Past years' AI seminars: 00 99 98 97
Other AI-related seminars at CMU: CALD Seminar | RI Seminar | LTI Seminar | CNBC Seminar | VASC Seminar | PITT ISP Seminar






Sven Koenig

Georgia Tech

Greedy On-Line Planning  Note: Jean Harpley ( will coordinate Sven’s schedule.


Rakesh Vohra

Northwestern U., MEDS

Linear Programming and Vickrey Auctions


Latanya Sweeney

CMU (Heinz & SCS)

Data Privacy, National Security and Computational Solutions


Craig Boutilier

U. Toronto, Computer Science

A Generalized Bidding Language for Combinatorial Auctions


Tom Dietterich

Oregon State University, Computer Science

Support Vector Methods for Reinforcement Learning


NSH 1507, 10:00 am

Ben Kuipers

UT Austin, Dept of Computer Sciences


Learning the Cognitive Map and its Foundations.  Note the exceptional time and place of the talk.  Ben’s schedule will be coordinated by Illah Nourbakhsh (


Wean 4601,

10:30-11:30 am

Lynne Parker

Oak Ridge National Laboratory

Towards Cooperative Robot Teams in Complex Site Preparation Tasks.  Note: Monica Hopes ( will coordinate the schedule


Dov Samet

Hebrew University

Learning to play games in extensive form by valuation


Tristan Cazenave

University of Paris 8, Computer Science

Search algorithms for computer Go & Go playing session


Diane Litman

University of Pittsburgh

Learning, Adaptation, and Personalization in Spoken Dialogue Systems (Ariadna Font Llitjos will coordinate the schedule)


Tom Mitchell

CMU School of Computer Science

AI and the Impending Revolution in Brain Science


Vincent Conitzer & Cuihong Li

CMU, Computer Science Department & CMU RI, GSIA

Complexity of Manipulating Elections with Few Candidates.  (30 minute practice run of a talk to be given in the oral presentation track at AAAI-02.  Paper by Conitzer & Sandholm).  Also: Algorithm for Combinatorial Coalition Formation and

Payoff Division in an Electronic Marketplace. (30 minute practice run of a talk to be given at AAMAS-02.  Paper by Li & Sycara).



Manuela Veloso

CMU Computer Science Department

Multi-Robot Team Coordination and Learning in Adversarial Environments

The AI seminars are open to the public and will be held on most Tuesdays at 3:30pm in Wean Hall, Carnegie Mellon University.  Special AI seminars can be arranged for visitors on dates other than Tuesdays. The schedule will be updated daily. To volunteer to give an AI seminar or to nominate an outside speaker, contact Dr. Tuomas Sandholm at

All AI seminars will be posted to both cboards and bboards (cs & robotics).   Meanwhile, if you would like to/not to be reminded by email, please subscribe/unsubscribe ai-seminar-announcements mailing list by sending email to Simply put "Subscribe/Unsubsribe ai-seminar-announcements" in the subject of your message.


10/16/01 – Sven Koenig   Georgia Institute of Technology, College of Computing


Greedy On-Line Planning

Autonomous agents must be able to make good decisions in complex situations that involve a substantial degree of uncertainty, yet find solutions in a timely manner despite a large number of potential contingencies. Examples include mobile robots and decision-support systems for crisis situations.

In this talk, I will describe and analyze greedy on-line planning methods for such agents. These methods are based on two principles. One principle is to restrict the search to a small neighborhood of the current state of the agent, resulting in tractable planning methods that do not need to be in control of the agents at all times and are thus easy to integrate into complete agent architectures. The other principle is to perform incremental heuristic searches, resulting in planning methods that can solve several similar planning tasks faster than can be done by repeatedly planning from scratch. I will talk about algorithms, their analysis (including complexity results), and their integration into complete agent architectures, using robot mapping and localization tasks as examples. If time permits, I will also apply some of the insights to reinforcement learning and discuss why many reinforcement learning methods do not do a good job at selecting actions during learning.

This is joint work with Craig Tovey, Maxim Likhachev, and David Furcy.

Note: Jean Harpley ( will coordinate Sven’s schedule.


Sven Koenig graduated from Carnegie Mellon University in 1997 and is now an assistant professor at the College of Computing of Georgia Tech. His research centers around techniques for decision making that enable situated agents to act intelligently in their environments and exhibit goal-directed behavior in real-time, even if they have only incomplete knowledge of their environment, limited or noisy perception, imperfect abilities to manipulate it, or insufficient reasoning speed. More information can be found at


10/23/01 – Rakesh Vohra   Northwestern University, Managerial Economics and Decision Science (MEDS)


Linear Programming and Vickrey Auctions


The Vickrey sealed bid auction occupies a central place in auction theory because of its efficiency and incentive properties. Implementing the auction requires the auctioneer to solve n+1 optimization problems, where n is the number of bidders. In this talk I survey various environments (some old and some new) where the payments bidders make under the Vickrey auction correspond to dual variables in certain linear programs. Thus, in these environments, at most two optimization problems must be solved to determine the Vickrey outcome. Furthermore, primal-dual algorithms for some of these linear programs suggest ascending auctions that implement the Vickrey outcome.


This talk assumes no prior knowledge of auction theory. It is based on joint work with  Sushil Bikhchandani, Sven de Vries and James Schummer.



John L. and Helen Kellogg Professor of

Managerial Economics and Decision Science (MEDS)

Kellogg School of Management

Northwestern University


Home page:


Paper at



10/30/01 – Latanya Sweeney   CMU (Heinz School of Public Policy and School of Computer Science)


Data Privacy, National Security and Computational Solutions


Society is experiencing exponential growth in the number and variety of

data collected on individuals. This happens at a time when more and more

historically public information is  also electronically available. When

these data are linked together, they provide an electronic shadow of a

person or organization that is as identifying and personal as a fingerprint

even when the information contains no explicit identifiers, such as name

and phone number.  Other distinctive data, such as birth date and ZIP code,

often combine uniquely and can be linked to publicly available information

to re-identify individuals. The result provides widespread access to

strategic and sensitive information about the lifestyles, health, and

behaviors of people. In this talk, I will examine strategies for learning

sensitive and strategic information about individuals from disparate pieces

of information and will examine data surveillance as a terrorism and as a

counterterrorism weapon.  Data privacy is an emerging area of computer

science for studying computational solutions for sharing person-specific

data such that data remains practically useful while also providing

guarantees of anonymity. This talk examines how the development of data

privacy solutions not only effects societal policies and practices, but

also enhances the knowledge of computer science as well. This talk ends

with  discussion about the implications of having so much person-specific

data collected and shared on the future of terms like liberty, freedom and




Latanya Sweeney recently graduated with a Ph.D. in computer science from

MIT (being the first black woman to do so). She is now an Assistant

Professor of Computer Science and of Public Policy here in CALD and the

Heinz School.  At CMU, she has also started the Laboratory for

International Data Privacy which works on real-world data sharing problems

with stakeholders. Her work on data privacy has received numerous awards

from various disciplines including the Patient Advocacy Award by the

American Psychiatric Association and First Prize by the American Medical

Informatics Association. She has been invited to speak around the world and

before U.S. Senate committees. More information can be found at and



11/6/01 – Craig Boutilier   University of Toronto, Dept of Computer Science


A Generalized Bidding Language for Combinatorial Auctions


Combinatorial auctions provide a valuable mechanism for the

allocation of goods in settings where buyer valuations exhibit

complex structure with respect to substitutability and

complementarity.  Most algorithms are designed to work with

explicit bids for concrete bundles of goods. However, logical

bidding languages allow the expression of complex utility

functions in a natural and concise way.


In this talk, I introduce a new, generalized language where bids

are given by propositional formulae whose subformulae can be

annotated with prices. This language allows bidder utilities to

be formulated more naturally and (in some cases, exponentially)

more concisely than existing languages.  I will also describe

the computational advantages of using this language in winner

determination, both from the perspective of integer programming

and stochastic local search.


This talk describes joint work with Holger Hoos.



11/13/01 – Tom Dietterich  Oregon State University, Computer Science


Support Vector Methods for Reinforcement Learning


This talk will address two aspects of value function approximation for

reinforcement learning (RL).  First, most online RL algorithms work by

incrementally solving the Bellman equation.  In large RL problems,

function approximators must be employed to approximate the value

function.  These approximations typically prevent the Bellman equation

from being satisfied.  The first question addressed in this talk is

whether the Bellman equation is still useful for large-scale RL.


Second, most online function approximators have a fixed number of

basis functions (and hence, a fixed complexity or VC dimension).  In

supervised learning, we know that it is important to adapt the

complexity of the function approximator to the complexity of the

function being approximated.  The second question addressed in this

talk is how to automatically tune function approximator complexity for



We will present three formulations of the value function approximation

problem using techniques inspired by support vector machines.  These

three formulations are all batch-incremental algorithms that can tune

the complexity of the function approximator (as measured by the number

of strength of the support vectors) to the complexity of the value

function.  One formulation is based on supervised regression, a second

formulation is based on the Bellman equation, and the third

formulation is based on Leeman Baird's advantage learning.

Experimentally, we show that all three of these formulations work

well, but that the Bellman formulation gives slightly better

performance and is more reliable.  Hence, we conclude that the Bellman

equation is still useful even with value function approximation.




Tom Dietterich is Professor of Computer Science at Oregon State

University, where he has worked on many aspects of machine learning

since completing his PhD at Stanford in 1984.  His contributions

include the method of error-correcting output codes for converting

multi-class classification problems into binary classification

problems; the formulation and solution of multiple-instance learning

with application to drug activity preduction; the development of the

MAXQ formalism for hierarchical reinforcement learning; an approach to

applying reinforcement learning to discover search control heuristics

for job-shop scheduling; and the development of statistical tests and

other methodological tools for machine learning research.  Working

with his graduate students, he is currently studying cost-sensitive

supervised learning, spatio-temporal learning, and reinforcement

learning.  He has also served as Executive Editor of Machine Learning

(1991-1998), Founding Action Editor of the Journal of Machine Learning

Research (2000-present), Program Chair and General Chair of the Neural

Information Processing Systems Conference (NIPS-2000 and NIPS-2001),

and Program Co-Chair of the National Conference on Artificial

Intelligence (AAAI-1990).  He was elected a Fellow of the AAAI in




11/26/01 – Ben Kuipers  UT Austin, Computer Science


10:00am, NSH 1507


Learning the Cognitive Map and its Foundations


William James [1890] wrote, ``The baby, assailed by eyes, ears, nose,

skin, and entrails at once, feels it all as one great blooming,

buzzing confusion.''  Similarly, we imagine a robot born into an

unknown environment with an unknown set of sensors and effectors.  How

can it first learn the properties of its sensorimotor system, and then

learn a useful cognitive map of its world?


Our Spatial Semantic Hierarchy [Kuipers, AIJ, 2000] provides the

target for this learning process.  The SSH is a hierarchy of different

representations for knowledge of space, with different expressive and

inferential capabilities.  The control level defines continuous

control laws linking locally distinctive states.  These patterns of

reliable continuous behavior are abstracted to causal schemas in which

states are linked by discrete actions, supporting the creation of

symbolic causal and topological maps.


The goal of our learning process is the identification of a reliable

set of perceptual features and primitive motor commands that can

support the definition of trajectory-following and hill-climbing

control laws.  Once we can define the SSH control level, the rest of

the cognitive map can be built on that foundation.  I will describe

work that solves this problem for a simple simulated robot, and

current directions of research with physical robots in real




Benjamin Kuipers is Professor of Computer Sciences at the University

of Texas at Austin.  He investigates the representation of commonsense

and expert knowledge, with particular emphasis on the effective use of

incomplete knowledge.  He received the B.A. in Mathematics from

Swarthmore College, and the Ph.D. in Mathematics from MIT.  He has

held research or faculty appointments at MIT, Tufts University, and

the University of Texas.  His research accomplishments include

developing the TOUR model of spatial knowledge in the cognitive map,

the QSIM algorithm for qualitative simulation, Access-Limited Logic

for knowledge representation, and the Spatial Semantic Hierarchy model

of knowledge for robot exploration and mapping.  He served as

Department Chairman 1997-2001, and is a Fellow of AAAI and IEEE.



12/11/01 – Lynne Parker  Computer Science and Mathematics Division at Oak Ridge National Laboratory


10:30am, Wean Hall 4601


Towards Cooperative Robot Teams in Complex Site Preparation Tasks


The application of robot teams to tasks requiring terrain alteration is very challenging, due to the unpredictable nature of the robot-terrain interaction, as well as the robot-robot interaction.   Two examples of terrain alteration tasks, also known as site preparation tasks, are surface coal mining and planetary site preparation for human missions to Mars.  These tasks require the terrain surface to be altered or smoothed to obtain a specified profile.  The complete development of multi-robot solutions to these complex tasks requires addressing a number of issues in multi-robot control, including cooperative task allocation, 3D multi-robot path planning, multi-robot localization, autonomous cooperative elevation map generation, robot team behaviors for terrain alteration, and techniques for human control of multi-robot teams. To date, little research has addressed the ability of robot teams to solve these types of complex missions involving many autonomous control issues.  Our objective is to develop a complete multi-robot system that can solve these types of integrated cooperative tasks involving a number of autonomous control techniques in challenging application domains.  In this talk, I will discuss our progress to date towards reaching this objective.  I will describe a number of the individual techniques we are developing to address each of these cooperative control issues as well as the overall framework for the integration of these techniques.  I will present preliminary results of aspects of this research in simulation as well as on physical robot teams.



Dr. Lynne E. Parker is a Distinguished Research and Development Staff member in the Computer Science and Mathematics Division at Oak Ridge National Laboratory .  Dr. Parker received her Ph.D. degree in computer science in 1994 from the Massachusetts Institute of Technology (MIT), from the Artificial Intelligence Laboratory.   Her research is focused on the development and implementation of robotic control architectures that facilitate fault tolerant, cooperative control and learning in multi-robot teams.   For this research, she was awarded the 1999 DOE Office of Science Early Career Scientist Award, and the 1999 U.S. Presidential Early Career Award for Scientists and Engineers.  She also received a 2000 UT-Battelle Technical Achievement Award for Significant Research Accomplishments.  Dr. Parker is an active leader in her field, and is a frequent invited speaker at international conferences, workshops, and universities.  She is the Program Vice-Chair for the Americas for the 2002 IEEE International Conference on Robotics and Automation.  She is also guest co-editing a special issue of IEEE Transactions on Robotics and Automation on the topic of Multi-Robot Systems, to appear in 2002.  She and Tucker Balch have edited a book to be published in early 2002 entitled Robot Teams:  From Diversity to Polymorphism.  She is a member of IEEE, AAAI, ACM, and Sigma Xi.

2/19/02 – Dov Samet  Hebrew University

Learning to play games in extensive form by valuation


A valuation for a player in a game in extensive form is an

assignment of numeric values to the players moves. The valuation

reflects the desirability moves. We assume a myopic player, who

chooses a move with the highest valuation. Valuations can also be

revised, and hopefully improved, after each play of the game.

Here,  a very simple valuation revision is considered, in which

the moves made in a play are assigned the payoff obtained in the

play. We show that by adopting such a learning process a player

who has a winning strategy in a win-lose game can almost surely

guarantee a win in a repeated game. When a player has more than

two payoffs, a more elaborate learning procedure is required. We

consider one that associates with each move the average payoff in

the rounds in which this move was made. When all players adopt

this learning procedure, with some perturbations, then, with

probability 1, strategies that are close to subgame perfect

equilibrium are played after some time. A single player who adopts

this procedure can guarantee only her individually rational



Joint work with P. Jehiel.


Paper available at



Professor Samet is the incumbent of the Louise Lea Flack

Chair in Game Theory and Interactive Decisions. He

is a Fellow of the Econometric Society and a member

of the editorial boards of International Journal of

Game Theory, and Games and Economic Behavior.  


3/12/02 – Tristan Cazenave University of Paris 8, Computer Science Department

Search algorithms for computer Go


Programming a computer to play the game of Go is much harder than

programming it to play other classical games. The large branching factor

on a 19x19 board (250 possible moves on average) prevents usual search

algorithms from reading deeply. Moreover, there is no simple evaluation

function. The evaluation of a position depends on deep tactical reading,

on heuristic knowledge and on the difficult analysis of the relations

between the groups of stones.  In this talk we present some search algorithms

that are much more effective than basic search algorithms for solving tactical

Go problems.  They are much more selective than usual Alpha-Beta, they are more

reliable than the other selective search algorithms, and they only rely

on simple game dependent knowledge. They also work in other games than

the game of Go.


Tristan Cazenave is an associate professor of computer science at the

University of Paris 8. He received his Ph.D. in computer science from

Paris 6 University in 1996. His research is in the areas of search and

learning in computer games, and problem solving. He started programming

games at 15, and could never stop. He is the author of the Go program



Home page:


After the talk, Tuomas Sandholm will host a Go playing session in the CS lounge (5-7pm).  Professor Cazenave will be available to play.


3/19/02 – Diane Litman  University of Pittsburgh, Dept. of Computer Science and Learning Research and Development Center

Learning, Adaptation, and Personalization in Spoken Dialogue Systems


Learning, adaptation, and personalization are topics of current

research interest in spoken dialogue systems.  Applications range from

the use of reinforcement learning to optimize low-level dialogue

strategy parameters, the use of rule induction to trigger system

adaptation of more global dialogue strategies, and the use of

user-controlled adaptation to personalize and optimize the system.  In

this talk, I will describe the use of learning and personalization in

two rather different dialogue systems.  Our NJFun system provides

spoken telephone access to a database of things to do in NJ, and uses

reinforcement learning to tune dialogue strategy parameters in

response to dialogue data obtained in a controlled user study.  Our

CobotDS system provides spoken telephone access to the internet chat

environment LambdaMOO, and offers features such as personalized

grammars for speech recognition and personalized summarization.

Together, these systems offer a case study in the many issues that

arise in making learning and personalization effective and acceptable

to users of spoken dialogue systems.



Diane Litman joined the University of Pittsburgh in Fall 2001, as both an

Associate Professor of Computer Science, and a Research Scientist with the

Learning Research and Development Center (LRDC).  Diane moved here from

New Jersey, where from 1985-2001 she was a member of the Artificial

Intelligence Principles Research Department, AT&T Labs - Research

(formerly Bell Laboratories). From 1990-1992, she was also an Assistant

Professor of Computer Science at Columbia University. Diane received her

Ph.D. and M.S. in Computer Science from the University of Rochester, and

her A.B. in Mathematics and Computer Science from the College of William

and Mary. Diane's research is in the area of artificial intelligence, and

includes contributions in the areas of computational linguistics,

knowledge representation and reasoning, natural language learning, plan

recognition, spoken language, and user modeling.

4/23/02 – Tom Mitchell  CMU, Computer Science

Artificial Intelligence and the Coming Revolution in Brain Science


The study of the human brain is undergoing a major revolution due to the recent

invention of new, highly precise techniques for measuring human and animal

brain activity. For example, functional Magnetic Resonance Imaging (fMRI) now

provides scientists a safe, non-invasive means of producing a three-dimensional

"movie" of human brain activity with a spatial resolution of 3mm, and a

temporal resolution of 500 milliseconds. As a result, scientists are able for

the first time to see the detailed patterns of cortical activity that

constitute human cognitive processes such as language processing, vision,

memory and problem solving. This talk will provide both a tutorial on fMRI

brain imaging and typical experimental results, and will examine the

significant role that artificial intelligence and computer science can play in

the coming revolution in brain science.



Tom M. Mitchell is the Fredkin Professor of Computer Science at Carnegie Mellon

University.  He is President of the American Association of Artificial

Intelligence (AAAI), author of the textbook "Machine Learning," and a member of

the National Research Council's Computer Science and Telecommunications Board.

During 1999-2000 he served as Vice President and Chief Scientist at WhizBang!

Labs, a company that employs machine learning to extract information from the

web.  Mitchell is Director of CMU's Center for Automated Learning and

Discovery, an interdisciplinary research center specializing in statistical

machine learning and data mining. His web address is


4/30/02, 3:30-4pm – Vincent Conitzer  CMU, Computer Science

Complexity of Manipulating Elections with Few Candidates


In multiagent settings where the agents have different

preferences, preference aggregation is a central issue.  Voting is a

general method for preference aggregation, but seminal results have shown

that all general voting protocols are manipulable.  One way to avoid

manipulation is by using voting protocols where determining a beneficial

manipulation is computationally hard.  Some earlier work has been done in

this area, but it was assumed that the numbers of both voters and

candidates are unbounded.  In this talk, I will present our new hardness

results for manipulation in more practical voting settings where the

number of candidates is small but the number of voters can be large. These

results can be used to differentiate voting protocols on the basis of the

difficulty of manipulating them.


Exceptionally, this talk will only last 30 minutes.  It is a practice run of a talk to be given in the oral presentation track of the National Conference on Artificial Intelligence (AAAI-02).  Paper: Complexity of Manipulating Elections with Few Candidates, by Vincent Conitzer and Tuomas Sandholm.



Vincent Conitzer is a Ph.D. student in Tuomas Sandholm's

Agent-Mediated Electronic Marketplaces Lab at Carnegie Mellon's Computer

Science Department.  His interests include computational aspects of issues

in economics as well as strategic issues in computer science.


4/30/02, 4-4:30pm – Cuihong Li  CMU, RI & GSIA

Algorithm for Combinatorial Coalition Formation and Payoff Division in an Electronic Marketplace


In an electronic marketplace, coalition formation allows buyers

to enjoy a price discount for each item, and combinatorial auction enables

buyers to place bids for a bundle of items that are complementary.

Coalition formation and combinatorial auctions both help to improve the

efficiency of a market, and they have received much attention from

economists and computer scientists.  But there has not been work studying

the situations where both coalition formation and combinatorial auctions

exist.  In this paper we consider an e-market where each buyer places a

bid on a combination of items with a reservation cost, and sellers offer

price discounts for each item based on volumes.  By artificially dividing

the reservation cost of each buyer among the items, we can construct

optimal coalitions with respect to each item.  These coalitions satisfy

the complementarity of the items by reservation cost transfers, and thus

induce the optimal solution.  We focus on the systems with linear price

functions and present a polynomial-time algorithm to find a semi-optimal

solution and a payoff division scheme that is in the core of the

coalition.  Simulation results show that the algorithm obtains a solution

close to the optimal value.


Exceptionally, this talk will only last 30 minutes.  It is a practice run of a talk to be given in the AAMAS-02 conference.  Paper by Cuihong Li and Katia Sycara.



Cuihong Li is currently a research assistant in Dr. Katia Sycara's Advanced Agent Technology Lab in Robotics Institute.  Bio:  1993-1998, BA in Automation, Dept. of Automation, Tsinghua University, China.  1998-2000, MS in Systems Engineering, Dept. of Automation, Tsinghua University, China.  2000-present, Doctoral student in Management of Automation and Manufacturing (Joint program between GSIA and Robotics Institute), GSIA, Carnegie Mellon University.  Research interest: Information Economics, Agent-Mediated Commerce, Supply Chain Management.


5/7/02 – Manuela Veloso CMU, Computer Science Department

Multi-Robot Team Coordination and Learning in Adversarial Environments


My long-term research passion is the study of complete autonomous

intelligent robots that can continuously perceive the world, act,

achieve goals in dynamic and uncertain environments, and learn to

improve their performance. Creating such effective robots, in

particular as members of a team in the presence of opponents, is a

challenging problem.


Robotic soccer has offered an interesting concrete environment for

research in multiagent planning, execution, and learning. With my

students, we have been pursuing research in robotic soccer in three

different technical setups: fully distributed multiagent simulation,

small wheeled robots with centralized perception, and fully autonomous

Sony legged robots. We have participated in the RoboCup international

competitions since 1997.


In this talk, I will do a short historical overview of the research

contributions and performance of our teams. I will then focus on some

of our recent multi-robot coordination and learning algorithms

specifically aimed at responding to adversaries. I will present some

of our underlying main research contributions, including different

multi-robot behaviors, a real-time path planning and replanning

algorithm, and a variable learning rate multiagent learning

algorithm. I will further discuss the role of coaching in multi-robot

systems.  I will concluce setting our multi-robot research goals in

perspective and discussing some of the fascinating open questions to

be addressed towards creating truly robust multi-robot teams.



Manuela Veloso is Associate Professor in the School of Computer

Science Department at Carnegie Mellon University.  She received her

Ph.D. in Computer Science from Carnegie Mellon University in 1992. A

native of Portugal, she received a B.S. in Electrical Engineering in

1980 and an M.Sc. in Electrical and Computer Engineering in 1984 from

the Instituto Superior Tecnico in Lisbon.  Veloso researches in the

area of artificial intelligence. Her long-term research goal is the

effective construction of intelligent agents where cognition,

perception, and action are combined to autonomously address planning,

execution, and learning tasks. She has developed robotic soccer teams

that have participated in the RoboCup international competitions in

three different categories, namely simulation software agents,

small-wheeled robots, and Sony four-legged robots.  Veloso received an

NSF Career Award in 1995 and the Allen Newell Medal for Excellence in

Research in 1997. She is the author of one book on "Planning by

Analogical Reasoning", editor of several other books, and the author

of over 100 technical journal and conference papers.  Veloso is the

Vice-President of the RoboCup International Federation and was the

General Chair for RoboCup-2001, held in Seattle, August 2001. More

details are available at