Course of study, specific field and term:

• Master Entrepreneurship in Digital Technologies 2020 (advanced module), technology field computer science, arbitrary semester
• Master Computer Science 2019 (optional subject), Canonical Specialization Data Science and AI, 1st or 2nd semester
• Master IT-Security 2019 (advanced module), Elective Computer Science, 1st or 2nd semester
• Master Computer Science 2019 (optional subject), advence module, arbitrary semester
• Certificate in Artificial Intelligence (compulsory), Artificial Intelligence, 1st semester

Classes and lectures:

• CS4514 P: Lab course Information Retrieval Agents (practical course, 3 SWS)
• CS5131 T: Web Mining Agents (lecture with exercises, 6 SWS)

Moodle Page of the Course

Workload:

• 180 Hours private studies
• 135 Hours in-classroom work
• 45 Hours exam preparation

Contents of teaching:

• The term Intelligent Agents denotes the area dealing with modelling and investigating systems of rationally acting agents. The underlying notion of rationally takes into account on the hand the ability of constructing and following optimal plans for accomplishing given targets (such as optimizing a cost or utility function) and on the other hand, as recent research shows not les important, the ability to possibly adapt the targets such as to increase public welfare.
• In the module we are going to consider next to ideas and concepts of cooperation of agents from classical game theory and the classical theory of multi-agent systems and next to ideas from machine learning also ideas from adaptive model construction constraint by declarative specified norms (keywords: belief revision, formal ethics, deontic logics).
• The ideas, concepts, approaches, and theories taught in the course are illustrated with applications in the scenario of web-mining agents. Technical, economical, and social phenomena are considered from the perspective of systems in order to explain how humans can benefit from web resources. For this, the module Intelligent Agents introduces the foundations of analyzing and designing large connected systems and explains how agent that are autonomous but also constraint in their decisions by principles of public welfare can cooperate such that data can be transformed to information for humans according to formally specified requirements.
• In the project lab students use the usual (open source) data science related programming languages and tools in order to transfer the abstractions, concepts and results taught in the lecture into concrete software models and artefacts to be applied on big data.
• For more information see the module parts.

Qualification-goals/Competencies:

• Students gain deep knowledge and solid skills in the area of multi-agent systems, machine learning and information systems and information systems such that , say, newest achievements of web search engines (e.g. Google Knowledge Vault) can be analyzed and such that students can produce sustainable information systems for use in research and in industry.
• For more information see the module parts.

Grading through:

• Oral examination

Responsible for this module:

• Prof. Dr. rer. nat. habil. Ralf Möller

Teacher:

• Institute of Information Systems
• Prof. Dr. rer. nat. habil. Ralf Möller
• PD Dr. Özgür Özçep

Literature:

• see module parts

Language:

• offered only in English

Notes

(The module consists of CS5131 T and CS4514-P)
(Replaces CS4513-KP12)

A combination with the advance IFIS module Data Management (CS4508-KP12) is useful for studying aspects of distributed and mobile data management, and for performing complementary practical work in the field of parallel processing of large data volumes. In contrast to the mobile-data assumption in Data Management, it is assumed in Intelligent Agents that rather than data, interpretation processes are mobile in the form of agents. Agents have the task to autonomously determine and integrate a high-level data interpretation which is ultimately communicated to a user process.

Other complementary advanced modules such as CS4509-KP12 Internet Structures and Protocols or CS4511-KP12 Learning Systems offer interesting perspectives as well.

Prerequisites for admission to the examination may be scheduled at the beginning of the semester. When prerequisites are defined, they should be completed and positively evaluated before the initial examination.

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- depending on the module parts

Last updated:

9.9.2020