Intelligent Systems: Robots, Agents, and Humans

This course is a study of systems that exhibit intelligent attributes. We cover practical techniques for designing intelligent agents capable of planning, learning, and cooperation. There will be discussion of psychological/social ramifications of the use and creation of intelligent systems. Much of the course focuses on various challenge problems (e.g. the Trading Agent Competition and Robocup) that have been used to benchmark the performance of intelligent systems.

Prerequisites

• CAP5610 or equivalent machine learning/AI course

Objectives

• understand design issues facing the creators of intelligent systems
• understand research issues relating to the problem of creating human-like systems;
• learn practical techniques in planning, learning, three-tier architectures, and cooperation relevant to multi-agent and multi-robot systems;
• implement intelligent agents for a variety of domains;

Evaluation

• Written assignments (30%)
• Paper presentation (10%)
• Implementation assignments (30%)
• Final project (30%)

Topics

1. Agent Components
   • Planning
   • Reinforcement Learning
   • Multi-agent coordination
2. Autonomous Agent Challenge Problems
   • Trading Agent Competition
   • Agent Reputation and Trust Competition
   • Robocup
   • Urban Robot Rescue
   • AI in Games
3. Intro to Robotics
   • Path Planning
   • Localization
   • Multi-robot Coordination
   • Human-Robot Interfaces
4. Modeling Humans
   • Virtual Humans
   • Cognitive Agents
   • Psychological Models
5. Current Topics
   • Transfer Learning
   • Game Theory
   • Social Networks
   • Sensor Networks
   • Assistive Robotics

Homework

Schedule

1. Introduction
   • Reading: Katia Sycara, Multiagent Systems, AI Magazine 19(2): Summer 1998, 79-92
2. Multiagent Systems
   • Reading: D. Nau, Current trends in automated planning, AI Magazine, Winter 2007
   • Reading: J-P. Kelly, A. Boea, and S. Koenig, Offline planning with HTNs in Video Games, AIIDE 2008
3. Planning I
   • Reading: A. Blum and M. Furst, Fast planning through graph plan analysis, AI 90: 281-300
4. Planning II
   • Reading: A. Greenwald and P. Stone, Autonomous Bidding Agents in the Trading Agent Competition IEEE Internet Computing, 5(2):52.60, March/April 2001.
5. Trading Agent Competition
   • Reading: A. Greenwald and J. Boyan Bidding Under Uncertainty: Theory and Experiments In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (Jul 2004), pp. 209-216.
   • Reading: David Pardoe and Peter Stone. An Autonomous Agent for Supply Chain Management. In Gedas Adomavicius and Alok Gupta, editors, Handbooks in Information Systems Series: Business Computing, Elsevier, 2007
   • Reading: J. Reeder, G. Sukthankar, M. Georgiopoulos, and C. Anagnostopoulos, Intelligent Trading Agents for Massively Multi-player Game Economies. In Proceedings of AI in Interactive Digital Entertainment (AIIDE), October 2008
6. Agent Reputation and Trust Testbed
   • Reading: Teacy, W. T. L., T. D. Huynh, R. K. Dash, N. R. Jennings, J. Patel, and M. Luck. The ART of IAM: The Winning Strategy for the 2006 Competition, The Workshop on Trust in Agent Societies at The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-2007), Honolulu, Hawaii, USA, May 15, pp. 102-111
7. Robocup: Simulator and Urban Rescue
   • Reading: Peter Stone, Manuela Veloso, and Patrick Riley. The CMUnited-98 Champion Simulator Team. In M. Asada and H.Kitano, editors, RoboCup-98: Robot Soccer World Cup II Lecture Notes in Artificial Intelligence, pp. 61.76, Springer Verlag 1999.
   • Reading: Wang, J., Lewis, M., and Scerri, P. (2006). Cooperating robots for search and rescue, Proceedings of the AAMAS 1st International Workshop on Agent Technology for Disaster Management, 92-99.
8. Reinforcement Learning
   • Reading: Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4:237--285, May 1996
   • Reading: Peter Stone and Manuela Veloso. Team-Partitioned, Opaque-Transition Reinforcement Learning. In Minoru Asada and Hiroaki Kitano, editors, RoboCup-98 : Robot Soccer World Cup II, Lecture Notes in Artificial Intelligence, pp. 261.72, Springer Verlag, Berlin, 1999. Also in Proceedings of the Third International Conference on Autonomous Agents, 1999
9. Transfer Learning and CBR
   • Reading: Matthew E. Taylor and Peter Stone. Cross-Domain Transfer for Reinforcement Learning. In Proceedings of the Twenty-Fourth International Conference on Machine Learning, June 2007.
   • Reading: Matthew Molineaux, David W. Aha, Philip Moore, Learning Continuous Action Models in a Real-Time Strategy Environment, Proceedings of FLAIRS, 2008, pp.257-262
10. Robotics: Path Planning
    • Reading: S. Koenig and M. Likhachev, Real-time Adaptive A*, Proceedings of AAMAS 2006
    • Reading: S.M. LaValle and J.J Kuffner. Randomized kinodynamic planning. In Proc. IEEE Int'l Conf. on Robotics and Automation (ICRA'99), Detroit, MI, May 1999
11. Robotics: Multi-robot Coordination
    • Reading: M. Dias, M. Zinck, R. Zlot, and A. Stentz, Robust Multirobot Coordination in Dynamic Environments, In Proceedings of International Conference on Robotics and Automation, 2004
    • Reading: M. Koes, I. Nourbakhsh, and K. Sycara. Constraint Optimization Coordination Architecture for Search and Rescue Robotics, in Proceedings of ICRA 2006, Orlando, FL, May 2006, pp. 3977-3982.
12. Robotics: Localization and SLAM
    • Reading: D. Fox, W. Burgard, F. Dellaert, S. Thrun, Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, AAAI 1999
    • Reading: D. Haehnel, W. Burgard, D. Fox, and S. Thrun, An Efficient FastSLAM Algorithm for Generating Maps of Large-scale Cyclic Environments From Raw Laser Range Measurements, IROS-03.
13. Human-Robot Interaction
    • Reading: S. Thrun, M. Bennewitz, W. Burgard, F. Dellaert, D. Fox, D. Haehnel, C. Rosenberg, N. Roy, J. Schulte, and D. Schulz. MINERVA: A second generation mobile tour-guide robot, Proceedings of ICRA
    • Reading: C. Breazeal and B, Scassellati, Infant-like Social Interactions Between a Robot and a Human Caretaker To appear in Special issue of Adaptive Behavior on Simulation Models of Social Agents,
14. Emulating Humans: Motion and Emotion
    • Reading: G. Sukthankar, M. Mandel, and K. Sycara, Planning for Physically-Embodied Agents using Realistic Human Motion Models, In Simulation and Gaming Journal, Mar 2008
    • Reading: J. Gratch and S. Marsella, Tears and Fears: Modeling emotions and emotional behaviors in synthetic agents, Proceedings of AAMAS-2001
15. Emulating Humans: Teamwork
    • Reading: X. Fan and J. Yen. Realistic Cognitive Load Modeling for Enhancing Shared Mental Models in Human-Agent Collaboration. In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-07)
16. Human Activity Recognition
    • Reading: D. Sanchez, M. Tentori, and J. Favela, Activity Recognition for the Smart Hospital, IEEE Intelligent Systems

Individual Presentations Apr 17: Sean Mondesire

Apr 22: Robotic Demo Schedule
3-4pm:
4-5pm:
5-6pm:
Old course web page