Intelligent Systems: 2010-2011
OverviewThis is an introductory course into the field of artificial intelligence. The course focuses mainly on various search-based methods for problem solving. Emphasis is put on the design of heuristics that help dealing with large search spaces.
Learning outcomesBy attending the course the students should acquire a firm grasp of various search techniques and should be able to apply search in practice. Since search is such a fundamental technique in computer science, the material taught in the course is relevant in contexts other than artificial intelligence.
This course assumes that the students are familiar with the basics of propositional logic. Undergraduate students are expected to acquire the relevant expertise in the Logic and Proof course. MSc students are expected to have completed an equivalent course during their undergraduate education.
Students are expected to use Java in practicals. This course does not cover any aspects of Java programming: the students are expected to have learned Java elsewhere. No knowledge of Java or any other programming language is required in order to pass the exam: all programming (in Java or any other language) is limited to practicals.
|1||Introduction to Artificial Intelligence: definition of AI; Turing test; brief history of AI.|
|2-3||Problem solving and search: problem formulation; search space; states vs. nodes; tree search: breadth-first, uniform cost, depth-first, depth-limited, iterative deepening; graph search.|
|4||Informed search: greedy search; A* search; heuristic function; admissibility and consistency; deriving heuristics via problem relaxation.|
|5||Local search: hill-climbing; simulated annealing; genetic algorithms; local search in continuous spaces.|
|6-7||Planning: the STRIPS language; forward planning; backward planning; planning heuristics; partial-order planning; planning using propositional logic; planning vs. scheduling.|
|8-9||Dealing with geometry of physical agents: basic issues in robotics; degrees of freedom; Dijkstra’s shortest path algorithm; configuration spaces; Voronoi diagrams; skeletonization; potential field planning.|
|10||Constraint satisfaction problems (CSPs): basic definitions; finite vs. infinite vs. continuous domains; constraint graphs; relationship with propositional satisfiability, conjunctive queries, linear integer programming, and Diophantine equations; NP-completeness of CSP; extension to quantified constraint satisfaction (QCSP).|
|11-12||Solving CSPs: constraint satisfaction as a search problem; backtracking search; variable and value ordering heuristic; degree heuristic; least-constraining value heuristic; forward checking; constraint propagation; dependency-directed backtracking; independent subproblems; tree-like CSPs; acyclic CSPs; CSPs of bounded treewidth.|
|13-14||Playing games: game tree; utility function; optimal strategies; minimax algorithm; alpha-beta pruning; games with an element of chance.|
|15-16||Beyond classical search: searching with nondeterministic actions; searching with partial observations; online search agents; dealing with unknown environments.|
Problem solving via search. Uninformed, informed, and local search. Planning. Dealing with geometry of physical agents. Constraint satisfaction. Adversarial search. Searching with nondeterministic actions and partial observations.
Reading listS.J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Prentice-Hall, 2010.
Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.