Intelligent Agents: Reasoning, Planning, Perception and Cooperation

The course will deal with a selection of the topics listed below, varied over different realisations of the course.

Part I: Intelligent Autonomous AgentsSoftware Agents and embodied Agents: Autonomy. Agents as Intentional Systems. Deductive Reasoning Agents. Practical Reasoning Agents. Means--Ends Reasoning. Procedural Reasoning System. Reactive and Hybrid Agents. Communication and Cooperation. Ontologies. Speech Acts. Cooperative Distributed Problem Solving. Coordination between agents

Part II Elements of Agent Perception: Visual Perception. Auditory Perception and other Sensor Modalities (brief coverage). From Sensor Data to Percepts and Interpretations.

Part III Planning: Algorithms, Planners, and Plans. Discrete Planning. Motion Planning for Mobile Agents. Decision-Theoretic Planning. Sequential Decision Theory. Sensors and Information Spaces. Planning Under Sensing Uncertainty

KNOWLEDGE:

Thorough understanding of principles of intelligent agents. Thorough understanding of different architectural principles (cognitivistics / connectionist / hybrid) for the construction of agent systems, principles of agent communication, coordination and planning. Familiarity with both the theoretical concepts of AI and embodied agents as well as established principles for implementation of AI agents.

SKILLS:

Abitliy to critically evaluating different agent architectures and implementations. Ability to critically study research papers and other scientific literature and to recognise the individual value of a publication or claimed achievement in the regard topic area.

GENERAL COMPETENCE:

Ability to systematically conquer new knowledge areas and perceive and communicate the essential facts and contents. Skills in applying new acquired knowledge and proficiency in new areas and successfully complete advanced tasks and projects. Skills in scientific communication (written and oral) and in clearly conveying insights and achievements. Skills in contributing to multilateral scientific or technical discussions. Ability to contribute to innovative thinking and innovation processes.

• Seminars with lectures (8x90 min) and enbedded discussion (total class time: 180 min/session).
• Weekly reading assignments and writing tasks (typically: short essay on lecture-related topics).
• Conference style presentation by the participants on selected papers and/or own PhD work, related to the course topic area.
• Individual study on a research project, leading to 6 page paper, or a 12 page review paper.
• Final exam (oral).

Weighting: 40 / 30 / 30 %