Check here for updates regarding the course.
Intelligent agents are software programs that can sense their environment, choose rational actions based on their percepts, and execute these actions. If an agent does all of this without the aid of a human, then it is generally considered autonomous. Often, agents interact with other agents, either by cooperating or competing with each other; such environments are called multiagent systems. Agents can be embedded in completely electronic environments such as the Web or a simulation, or may actually be robots "living" in the real world. The potential applications of agents are numerous -- including web search assistants, travel advisors, electronic secretaries, bidders in on-line auctions, tutoring systems, and actors in games or simulations. Some have even predicted a future in which agent technology is embedded in everyday items, allowing household objects to coordinate actions in order to better serve the home owner. The course will cover the underlying theory of agents, the common agent architectures, methods of cooperation, and the potential applications for agents. In order to gain a better understanding of the concepts, students will construct their own agents for solving different types of problems.
For details about course content, grading, assignments, and office hours, see the class syllabus.
In addition to handing out assignments in class, electronic copies will be made available here.
Unless specified otherwise, all readings are from our textbook, An Introduction to MultiAgent Systems by Michael Wooldridge.
|Read Ch. 1-2 (pp. 1-45)||8/30|
|Read Ch. 3 (pp. 47-62)||9/4|
Read GOLOG: A logic programming language for dynamic domains by Levesque et al.|
Review Object-oriented design for agent simulation (Java documentation)
|Read Ch. 4 (pp. 65-86)||9/13|
|Read Plans and Resource-Bounded Practical Reasoning by Bratman, Israel, and Pollack.
Note: Figure 1 is missing, a copy can be found here.
Read Sect. 5.1-5.2 (pp. 89-97)
Read A Robust Layereed Control System for a Mobile Robot by Rodney Brooks
Read Sect. 5.3 (pp. 97-103)
Read Unifying Control in a Layered Agent Architecture by Fischer, Muller, and Pischel
Read Sect. 6.1-6.3 (pp. 105-113)
|Read Sect. 6.4-6.7 (pp. 114-126)||9/27|
|Read Sect. 9.1-9.2 (pp. 189-197)||10/2|
|Read Sect. 9.3-9.6.2 (pp. 197-210)||10/4|
|Read Sect. 9.6.3 - 9.7 (pp. 210-222)||10/11|
|Read Towards Flexible Teamwork by Milind Tambe||10/18|
|Read Sect. 7.1-7.2 (pp. 129-137)||10/23|
|Read Sect. 7.3-7.4 (pp. 137-160)||10/25|
|Read Sect. 8.1-8.2 (pp. 163-180)||10/30|
|Read Sect. 8.3-8.4 (pp. 180-187)|
Read An Introduction to the OWL Web Ontology Language by Jeff Heflin
|Read Sect. 12.1-12.7 (pp. 267-288)||11/8|
|Read The First International Trading Agent Competition: Autonomous Bidding Agents by Peter Stone and Amy Greenwald
Review the games (TAC SCM and TAC Classic) available from the TAC Website
|Read Searching for Walverine 2005 by Michael Wellman et al.
Read RoxyBot-06: An (SAA)2 TAC Travel Agent by Seong Jae Lee, Amy Greenwald, and Victor Naroditskiy
|Read The CMUnited-98 Champion Simulator Team by Stone, Veloso and Riley||11/27|
|Read Markov Games as a Framework for Multi-Agent Reinforcement Learning by Michael Littman|
Read Acting Optimally in Partially Observable Stochastic Domains by Cassandra, Kaelbling and Littman
Read A Scaleable Comparison-Shopping Agent for the World-Wide Web by Doorenbos et al|
Read Electric Elves: Applying Agent Technology to Support Human Organizations by Chalupsky et al.
Read A Plug-in Architecture for Generating Collaborative Agent Responses by Rich et al.|
Read Ch. 11 (pp. 245-265)