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STUDY GUIDE · GKU 6

Fundamentals of artificial intelligence study guide (Artificial Intelligence)

This unit is the core of an AI degree in one place. It starts with what AI is and where it came from, how to describe a problem's environment, and what kinds of agents act in it. It then turns to how an agent can know things — logic, frames and semantic networks — and how it can reason when it is not sure, with Bayes' rule and Bayesian networks. The last part is search: writing a problem as states and actions, the classic linear and binary searches, blind and heuristic search traced node by node on one small graph, and finally local search, constraint satisfaction and game search, compared side by side. Each section opens with a quick-revision card; worked examples run from easy to exam level.

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Structured by the official ETEC subject standard for this topic: every listed topic and learning outcome, with formulas, figures and worked examples. The full sections are available to members in the app.

SKU 6.1 · AI principles and techniques

  1. What AI is: definitions, techniques, applications and history

    The four ways textbooks define AI and why the rational-agent view won; the main techniques and where each is used; and the milestones from Turing's paper to today's language models.

  2. Characteristics of problems: describing the task environment

    PEAS descriptions and the seven dimensions — observable, agents, deterministic, episodic, static, discrete, known — that decide which methods can work on a problem.

  3. Intelligent agents: components and types

    An agent perceives, reasons and acts. Reactive agents map percepts to actions by rules, deliberative agents use a world model to look ahead, and learning agents improve from feedback — traced on a vacuum-cleaner agent.

  4. Intelligent behaviour: the Turing test and simulating intelligence

    What the Turing test measures and what it does not, its variants and critics (the total Turing test, the Chinese room, CAPTCHAs), and the main ways a machine can simulate intelligence.

SKU 6.2 · Knowledge representation and reasoning

  1. Logic for knowledge representation: propositional and first-order inference

    A knowledge base as logical sentences; entailment checked by truth tables; inference by modus ponens and resolution; first-order logic with quantifiers, unification and forward chaining.

  2. Frames, semantic networks and the semantic web; choosing a representation

    Structured representations for categories and objects: inheritance with defaults and overrides, RDF triples and ontologies, and how to choose between logic, frames, rules and probability for a given problem.

  3. Reasoning under uncertainty: Bayes’ rule and Bayesian networks

    Where uncertainty comes from, why logic alone cannot handle it, Bayes' rule with base rates, and exact inference by enumeration in a four-node Bayesian network.

SKU 6.3 · Search

  1. State-space search: formulating problems and plans

    The five parts of a search problem, how big a state space is, and classical planning, where actions with preconditions and effects turn a goal into a sequence of steps.

  2. Basic searching algorithms: linear and binary search

    How linear and binary search work, their exact comparison counts, a binary search traced probe by probe, and when sorting first pays for itself.

  3. Uninformed search: breadth-first, depth-first, uniform-cost and iterative deepening

    Blind strategies that differ only in which frontier node they expand next, traced on one weighted graph, with their completeness, optimality, time and memory.

  4. Informed search: greedy best-first search, the A-star algorithm and heuristic design

    Using an estimate of the remaining cost to steer search: greedy best-first search, A* with , what admissible and consistent heuristics guarantee, and how to build and compare heuristics.

  5. Local search, constraint satisfaction and game search; comparing search methods

    Hill climbing and simulated annealing, backtracking with forward checking and arc consistency, minimax with alpha–beta pruning, and a table that compares every search method in this part.

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