CMSC450 AI Tools
Interactive tools for CMSC450 (artificial intelligence): rational agents, task environments, search problems, and uninformed and informed search.
- Deterministic Can be sequential or episodic
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- Search 6 tools
- Constraint satisfaction
- Classical planning
- Multi-agent, strategic Can also be stochastic, partially observable
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- Minimax search, games
- Stochastic Episodic or sequential; rules known or unknown
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- Episodic Bayesian networks, pattern classifiers
- Sequential, known Markov decision processes
- Sequential, unknown Reinforcement learning
Introduction
Definitions of AI, the Turing test, and AI history · Chapter 1
- Approaches to AI Lays out the four definitions of AI (thinking or acting, humanly or rationally), poses the Winograd schema questions, and sorts AI applications into the four approaches on a shareable board. Lecture reference: Introduction to AI · slides 2–18
- AI history Charts the eras of AI from 1943 to the present on one timeline with the AI winters and dated events from the lecture, alongside the foundations of AI, historical themes, and the state of the art. Lecture reference: Introduction to AI · slides 19–27
Rational agents
Agents, PEAS, and environment types · Chapter 2
- Vacuum-cleaner agent Runs the reflex vacuum agent and other agent programs in the two-square vacuum world step by step, scores them with a performance measure, and compares their average scores over all initial states. Lecture reference: Rational Agents · slides 3–5 Lecture reference: Solving Problems by Searching · slide 8
- Task environments Compares task environments along the seven environment types in a table like slide 17, shows their PEAS descriptions, and lists the methods from the course preview that fit each one. Lecture reference: Rational Agents · slides 6–8 Lecture reference: Rational Agents · slides 9–18
Solving problems by searching
State spaces, tree search, and uninformed strategies · Chapter 3
- State spaces Formulates the example problems (Romania, the vacuum world, the 8-puzzle, robot motion planning) as search problems, draws their state spaces, lists what the successor function returns for any state, and grows a breadth-first or uniform-cost search outward from the start state. Lecture reference: Solving Problems by Searching · slides 2–26 Lecture reference: Uninformed Search · slide 40
- Tree and graph search Runs breadth-first, depth-first, depth-limited, iterative deepening, uniform-cost, greedy best-first, A*, and weighted A* search on a graph, one step at a time, with the state space, the search tree, and the frontier side by side. Lecture reference: Solving Problems by Searching · slides 27–43 Lecture reference: Uninformed Search · slides 3–44 Lecture reference: Informed Search · slides 7–38
- Comparing search strategies Lists the completeness, optimality, and time and space complexity of each search strategy as on the slides, runs BFS, DFS, IDS, UCS, greedy best-first, A*, and weighted A* on one problem side by side, and computes node counts for given b, d, and m. Lecture reference: Uninformed Search · slides 30–45 Lecture reference: Informed Search · slide 36 Lecture reference: Informed Search · slides 42–43
Informed search
Heuristics, greedy best-first search, and A* · Sections 3.5–3.6
- Heuristics Checks a heuristic on a graph problem: h(n) against the true cost h*(n), every edge for consistency, dominance and the maximum of two heuristics, and the paths A* tree search, A* graph search, and weighted A* return with it. Lecture reference: Informed Search · slides 25–30 Lecture reference: Informed Search · slides 35–38
- 8-puzzle Slides tiles on a 3 × 3 board, computes the misplaced-tiles (h1) and Manhattan-distance (h2) heuristics, checks solvability, and solves any board with BFS, IDS, greedy best-first, A*, and weighted A*, then steps through the solution. Lecture reference: Solving Problems by Searching · slide 10 Lecture reference: Informed Search · slides 32–37
- Path finding on a grid Runs breadth-first, depth-first, uniform-cost, greedy best-first, A*, and weighted A* search on a grid with walls you draw, one expansion at a time, alone or two side by side. Lecture reference: Informed Search · slides 23–24 Lecture reference: Informed Search · slides 38–40
Reference
Symbols, conventions, and lecture index