Intelligent Systems

core topics:

• search & heuristics
• knowledge
• adaptivity

Table of contents:

• Assessment information
• State Space Representations Intro
• State space search
  • Uninformed search strategies
    • Breadth-first (BF) search
    • Depth-first (DF) search
    • Depth-limited search
    • Iterative deepening search
  • Informed search (heuristics)
    • A Search
    • A* Search
  • Adversarial search
    • Minimax
      • Setup
      • Optimal strategies
      • Evaluation
    • Reducing problems of complexity with Minimax
      • Cutting off search:
      • Alpha-Beta pruning (efficient Minimax)
    • Search with no or partial information
      • Perfect information Monte Carlo sampling (rdeep)
    • Games with chance
  • Summary (Schnapsen)
  • Search direction
• Rational agents
  • Agents
  • Rationality
  • Task environments
  • Agent types
    • Simple Reflex
    • Reflex & State
    • Goal-Based
    • Learning
• Logical agents
  • What is logic
  • Syntax
    • Propositional logic (PL)
    • First order logic (FOL)
      • Basic elements:
      • Sentences
      • Quantification
        • Universal quantification
        • Existential quantification
        • Quantifier Duality
      • Decidability vs undecidability
      • Knowledge engineering in FOL
    • Choice of formalisms
    • Propositionalising FOL
      • Reduction to propositional inference
      • Universal instantiation (UI):
      • Existential instantiation (EI):
      • Applying in Schnapsen - Strategies (examples)
        • Play Jack
        • Play cheap
        • Play trump marriage
  • Semantics
    • Interpretations & Models
    • Entailment
    • Truth
    • Validity
    • Satisfiability
  • Calculus (algorithms for inference)
    • Properties of inference
    • Proof methods
      • Model checking & search
        • Truth Tables for inference
        • Effective proofs by model checking
        • Clause Normal Form (CNF)
        • DPLL algorithm
          • Heuristic search in DPLL
        • Satisfiability modulo theory
      • Rule-based reasoning
        • Inference rules
        • Searching for proofs
        • Forward and backward chaining
        • Resolution
• Probability and Uncertainty
  • Vagueness: Fuzzy Set Theory
    • Fuzzy sets
    • Fuzzy relations
    • Evaluation
  • Uncertainties: Probability Theory
    • General
    • Axioms of probability
    • Joint probability distributions
    • Bayesian networks
    • Evaluation of probabilities

• Machine Learning
  • Learning problems
  • Methodology
    • Data
    • Experimentation
    • Evaluation
  • Machine Learning Steps:
    • Choose the features
      • Inductive learning method
      • Classifying with naive Bayes
      • Clustering with K-nearest neighbor
      • Linear classifier
      • Support vector machine
    • Choose the model (model search)
      • Regression
      • Gradient descent
  • Neural Networks
    • Overview
    • Training neural networks
    • Autoencoders: a NN architecture
    • Trying it out
  • The promise of depth

• Ethics of AI
  • Sci-fi ethics (problems down the road)
  • Today’s problems
• Philosophy of AI