Module Manual

Modul CS4519-KP12

Intelligent Cooperative Agents (IntCoAgent)


1 Semester
Turnus of offer:

each winter semester
Credit points:

Course of studies, specific field and terms:
  • Master Artificial Intelligence 2023 (compulsory), Artificial Intelligence, 2nd or 3rd semester
Classes and lectures:
  • Intelligent Cooperative Agents (lecture, 6 SWS)
  • Intelligent Cooperative Agents (practical course, 2 SWS)
  • 240 Hours private studies
  • 30 Hours work on project
  • 90 Hours e-learning
Contents of teaching:
  • Agents, Mechanisms, and Collaboration: Intelligent agents and artificial intelligence / Game theory and social choice / Mechanism design, algorithmic mechanism design / Agent collaboration, rules of encounter / Continuous Space / Epistemic logic / Knowledge and seeing / Knowledge and time / Dynamic epistemic logic / Knowledge-based programs
  • Perception (Language and Vision): Information retrieval and web-mining agents / Probabilistic dimension reduction, latent content descriptions, topic models, LDA, LDA-HMM / Representation learning for sequential structures, embedding spaces, word2vec, CBOW, skip-gram, hierarchical softmax, negative sampling / Language models (1d-CNNs. RNNs, LSTMs, ELMo, Transformers, BERT, GPT-3/OPT, and beyond), Natural language inference and query answering / Computer Vision (2D-CNNs, Deep Architectures: AlexNet, ResNet) / Combining language and vision (CLIP (OpenAI) / LIT (Google) / data2vec (Facebook) / Flamingo (DeepMind), DALL-E and beyond) / Knowledge graph embedding with GNNs, combining embedding-based KG completion with probabilistic graphical models (ExpressGNN, pLogicNet), MLN inference and learning based on embedded knowledge graphs, GMNNs)
  • Planning, Causality, and Reinforcement Learning: Planning and acting with deterministic models, temporal models, nondeterministic models, probabilistic models / Standard decision making / Advanced decision making and reinforcement learning / Causal dependencies / Intervention / Instrumental variables / Counterfactuals / Causal planning / Causal reinforcement learning
  • For all topics listed in the course content under the bullet points, students will be able to name the central ideas, define the relevant terms in each case, and explain how associated algorithms work using examples of applications.
Grading through:
  • portfolio exam
Responsible for this module:
  • M Ghallab, D. Nau, P. Traverso: Automated Planning and Acting - Cambridge University Press, 2016
  • J. Pearl, C. Glymour, and N.P. Jewell: Causal Inference in Statistics--A Primer - Wiley, 2016
  • S.J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach - 4th Ed., Pearson, 2020
  • Y. Shoham, K. Leyton-Brown: Multiagent-Systems: Algorithmic, Game-Theoretic, and Logical Foundations - Cambridge University Press, 2009
  • offered only in English

Prerequisites for attending the module:
- None (The competencies of the modules listed under 'Requires' are needed for this module, but are not a formal prerequisite)

Prerequisites for the exam:
- 50% of online quiz points

Module exam(s):
CS4514-L1: Intelligent Cooperative Agents portfolio exam for a total of 100 points, divided as follows:
- 50 points for an e-test (oral or written).
- 50 points for a project presentation

Letzte Änderung: