BLG 622E - Robot Intelligence

Course Objectives

1.To introduce the advanced techniques within artificial intelligence (AI), with particular focus on automated planning, uncertainty modeling and multiagent/multirobot systems,
2.To give an ability to carry out advanced research within artificial intelligence,
3.To give an ability to apply the knowledge learned on intelligent robot system design and development,
4.To provide the principles and methodologies to contribute to the AI field by developing novel solutions.

Course Description

The course covers the following subjects: knowledge representation and reasoning for logical agents and robots, reasoning under uncertainty, automated planning for autonomous robots, conditional/continual planning, scheduling and optimization, integrated planning and scheduling, probabilistic reasoning for robots, probabilistic graphical models, Markov Decision Processes, Partially Observable Markov Decision Processes, reinforcement learning, multiagent/multirobot systems, resource allocation, distributed artificial intelligence algorithms. These subjects are investigated in the context of intelligent robotic systems.