Intelligent Agents (in English):Agents, Mechanisms, and Collaboration / Agent Perception: Language and Vision / Actions Planning, Reinforcement-Learning / Causality (40 Vorlesungen, Universität zu Lübeck 2019-2024)

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Erscheinungsjahr:
2024
Medientyp:
Text
Beschreibung:
  • 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
Lizenz:
  • info:eu-repo/semantics/openAccess
Quellsystem:
Forschungsinformationssystem der UHH

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oai:www.edit.fis.uni-hamburg.de:publications/8821c826-d815-4eca-8ce1-edaa1cb83189