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Intelligent Agents


Content: Introduction to Agents.

Course Description: An agent is an entity that receives percepts from the environment in which it is operating and applies actions to the environment in order to achieve its goals. The notion of an agent provides a unifying conceptual framework for current research in artificial intelligence.

In these three lectures, I will introduce the basic ideas of agents, describe some agent architectures, and comment briefly on relevant philosophical and historical issues.

Lecturer:

John Lloyd is a professor in the Computer Sciences Laboratory at The Australian National University. His research interests are in computational logic, including agents, symbolic machine learning, and declarative programming languages. He is the author of "Foundations of Logic Programming", the main reference on theoretical issues in logic programming, and three other books. His current research is concerned with the foundations and architectures for agent systems.


Search and Games


Content: Search and Games

Course Description: Search is a major direction in current AI research and a powerful solving technology in a wide range of real-life problems. This course focuses on single-agent search techniques. Pathfinding in games is used as an application domain.

Lecturer:

Adi Botea has obtained his PhD degree at the University of Alberta, Canada. He holds a researcher position at NICTA, Canberra Research Lab, and an adjunct position at the Australian National University. His research interests include AI planning, search and games.


Knowledge Representation and Reasoning


Content: Knowledge Representation and Reasoning

Course Description: Research in knowledge representation and reasoning has a long history in artificial intelligence and logic-based approaches have played a major part in the fields development. In this course we will survey logic-based in KRR from non-monotonic logics though to description logics and the semantic web.

Lecturer:

Maurice Pagnucco gained his PhD from the University of Sydney. He is an Associate Professor in Computer Science and Engineering. His research interests are in knowledge representation and reasoning particularly in belief change and cognitive robotics.


Artificial Intelligence Planning


Content: AI Planning.

Course Description: The course presents the most important approaches to state space traversal used in planning, including techniques based on propositional satisfiability testing, heuristic state-space search, and logic-based data structures like binary decision diagrams. The main applications of these techniques in classical planning and in more complex forms of planning is discussed.

Lecturer:

Jussi Rintanen got his PhD degree at the Helsinki University of Technology in 1997, held research and academic positions at the universities of Ulm and Freiburg (Germany) until 2005, and is currently a principal researcher at NICTA, Canberra.


Universal Artificial Intelligence


Title: Universal Artificial Intelligence

Content: Occam's razor, Kolmogorov complexity; Bayesian probability theory; sequential decision theory.

Course Description: The dream of creating artificial devices that reach or outperform human intelligence is many centuries old. In this course I will present an elegant parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment that possesses essentially all aspects of rational intelligence. The theory reduces all conceptual AI problems to pure computational questions.

Lecturer:

Marcus Hutter is Associate Professor in the RSISE at the Australian National University in Canberra and NICTA adjunct.