Course - Intelligent Agents: Reasoning, Planning, Perception and Cooperation - DT8124
DT8124 - Intelligent Agents: Reasoning, Planning, Perception and Cooperation
About
Examination arrangement
Examination arrangement: Aggregate score
Grade: Passed / Not Passed
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Individual report | 40/100 | |||
| Oral exam | 30/100 | 30 minutes | ||
| Practical training | 30/100 |
Course content
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 Foundations of Agent Perception: Visual Perception. Auditory Perception. Other Sensor Modalities. 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
Learning outcome
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. 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.
Learning methods and activities
Seminars with lectures (6x90 min) and conference style presentation of the participants on selected papers on 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 %
Further on evaluation
Evaluation form:
A: 1 individual presentation (conference style) in weekly seminars,
B: 1 report (either a short report on an own research project, or a longer review paper on the state of the art in a selected area.)
C: A final oral exam.
Grades: Pass / Fail.
The available credit points in the three exam parts are 30 for the seminar presentation, 40 for the report, and 30 for the final exam (other credit point assignments are possible, but will be proportional to the 30/40/30 scheme.) If the student fail he/she will be offered a resit for parts B and/or C. Time of resit will be determined by the teacher.If fail after resit the course need to be retaken. Retake of the course will require new participation/deliverables in all activities.
Specific conditions
Admission to a programme of study is required:
Computer Science (PHCOS)
Recommended previous knowledge
The course is primarily, but not exclusively, meant for PhD students with a Masters degree in Computer Science or Electrical Engineering or Engineering Cybernetics. The students should preferrably have successfully taken courses in at least the foundations of Artificial Intelligence (AI), meaning both classical symbolic / cognitivistic AI as well as the foundations of machine learning. Further recommended prior knowledge: AI Programming. Basic physics (mechanics, foundations of electronics). Prior knowledge in pattern recognition is considered useful.
Required previous knowledge
Masters degree or equivalent in Computer Science, Electrical Engineering, or EngineeringCybernetics. Solid proficiency in engineering mathematics / statistics an a University level. Proficiency in programming in a higher level language. Successful passing at least a 'Foundations of AI' course or a Robotics course.
Course materials
Michael Wooldridge: An Introduction to Multiagent Systems (Second Edition)
Chapters: / Introduction / Software 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 10. Communication and CooperationOptional reading: Cooperative Distributed Problem Solving / Coordination between agents
Steven M. LaValle: Planning algorithms .Available as printed book or online from http://lavalle.pl/planning/ Topic selection: 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
The above is a catalog of topics from which a selection will be made each time the course is held, in order to adapt to the specific needs of the participants, reflected by their PhD work areas. The focus areas of each year's course are determined accordingly.
These book chapters can be complemented by review or tutorial articles on the same topic areas selected individually for each pass of the course.
No
Version: 1
Credits:
7.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Language of instruction: English
Location: Trondheim
- Computer and Information Science
- Computer Science
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD Individual report 40/100 INSPERA
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Room Building Number of candidates - Autumn ORD Practical training 30/100
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Room Building Number of candidates - Autumn ORD Oral exam 30/100
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Room Building Number of candidates - Spring ORD Individual report 40/100 INSPERA
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Room Building Number of candidates - Spring ORD Practical training 30/100
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Room Building Number of candidates - Spring ORD Oral exam 30/100
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Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"