Intelligent Agents (in English)

• R. Möller
• T. Braun
• M. Gehrke
• Ö. Özçep

2024, Präsentationen/Dias

• Endgültig publizierte Version

Zusammenfassung

Agents, Mechanisms, and Collaboration

o Intelligent agents and artificial intelligence

o Game theory and social choice

o Mechanism design, algorithmic mechanism design

o Agent collaboration, rules of encounter

o Epistemic logic intro

o Knowledge and seeing

o Knowledge and time

o Dynamic epistemic logic

o Doxastic Logic

o Justification Logic

o Knowledge Based programs

o Excursion Fourier Analysis (Proof of Arrows Theorem)



Perception of Agents (Language and Vision)

o Information retrieval and web-mining agents

o Probabilistic dimension reduction, latent content descriptions, topic models, LDA

o Representation learning for sequential structures, embedding spaces, word2vec, CBOW, skip-gram, hierarchical softmax, negative sampling

o Multi-relational latent semantic analysis

o Language models (1d-CNNs, RNNs, LSTMs, ELMo, Transformers, BERT, GPT-3, T5, and beyond), natural language inference and query answering, reinforcement learning (InstructGPT), in-context learning, ChatGPT

o LaMDA, Grounding, embedding knowledge graphs into language models, GNNs

o Vision and Language (2D-CNNs: AlexNet, ResNet / Transfer Learning / ViLBERT / CLIP)

o Generative vision models (DALL-E and beyond) VQ-VAE/d-VAE, DALL-E's transformer

o Summary: Agents and Perception



Actions of Agents (Planning, Causality, and Reinforcement Learning)

o Introduction (Agent, planning, and acting)

o Deterministic (State-variable representation, forward state-space search, heuristic functions, backward search, and plan-space search)

o Temporal (Temporal representation, planning with temporal models, constraint management, and acting with temporal models)

o Nondeterministic (Planning problem, and/or graph search, determination, and online approaches)

o Probabilistic (Stochastic shortest-path problems, heuristic search algorithms, and online approaches including reinforcement learning)

o Decision Making
- Foundations (Utility theory, Markov decision processes, and reinforcement learning)
- Extensions (Partially observable MDPs and decentralised POMDPs)
- Structure (Lifted DecPOMDPs, Factored MDPs, and first-order MDPs)

o Human-aware (Mental models, interpretable behaviour, and explanations)



Causality

o Introduction to causal representations, causal leanring

o Intervention

o Instrumental variables

o Counterfactuals