Program of Study. Artificial Intelligence 1. Shane Torbert TJHSST

Transcription

1 Program of Study Artificial Intelligence 1 Shane Torbert TJHSST

2 Course Selection Guide Description for 2011/2012: Course Title: Artificial Intelligence 1 Grade Level(s): Unit of Credit: 0.5 Prerequisite: AP Computer Science A plus Data Structures Course Description: Students study AI techniques in a variety of contexts with an emphasis on generalizing search algorithms. Topics include graphs, heuristics, optimization, recursion, pruning, and games. Programming assignments include word ladders, navigating across Romania, sliding-tile puzzles, N-queens and other local search, GHOST, Tic-Tac-Toe and Reversi. The programming language is Python.

3 Course Syllabus: Use the currently approved course syllabus that meets all the standard requirements. See Syllabus must haves on BlackBoard>TJ Science & Tech>ST Div Documents.

4 Program of Study: CS.AI1 Standard 1 Essential UNDERSTAND THAT GRAPHS STORE INTERCONNECTED DATA The student will investigate and understand that a graph data structure stores interconnected data at the nearest-neighbor level and that additional processing may reveal larger-scale connections. AI1.1.a,b,c: Interconnected data may be structured or unstructured. Unstructured data may be represented as a graph rather than a grid. Graphs are useful in determining the overall connectedness of a large, complex set of data. AI1.1.d,e,f: Graph algorithms should be developed using small test cases. Graph algorithms should then be tested using large test cases. Performance may be measured by runtime and memory usage. Benchmark 1.a Essential Investigate and Understand Designated Lab Techniques The student will investigate and understand designated lab techniques. Indicator 1.a.1 Essential Demonstrate basic lab techniques Demonstrate the following basic lab techniques: reading in a large dataset from a file, storing the data as a graph in an adjacency list, finding paths (possibly random for small test cases) from one node to another within the graph. Benchmark 1.b Essential Investigate and Understand the Use of Search Algorithms The student will investigate and understand the use of search algorithms. Indicator 1.b.1 Essential Identify and implement uninformed search algorithms Identify and implement uninformed (blind) search algorithms: depth-first search, breadth-first search, and depth-first search with iterative deepening. Benchmark 1.c Essential Investigate and Understand the Use of Optimal Search Algorithms The student will investigate and understand the use of optimal search algorithms. Indicator 1.c.1 Essential Assess the total cost of a path upon weighting edges in a defined manner Assess the total cost of a path generated by a blind search using a defined edge weighting system. Indicator 1.c.2 Essential Identify and implement an optimal blind search algorithm Identify and implement an optimal blind search algorithm such as the uniform cost search, using a heap data structure for efficient queue processing. Indicator 1.c.3 Essential Identify and implement an admissible and useful heuristic Identify and implement an admissible (underestimate) and useful heuristic: where costs scale with total distance, the straight line distance may be used. Indicator 1.c.4 Essential Identify and implement an optimal, informed search algorithm Identify and implement an optimal, informed search algorithm such as the A-star search, using a heap data structure for efficient queue processing. Benchmark 1.d Essential Investigate and Understand the Performance Scaling of Graph Algorithms The student will investigate and understand the performance scaling of graph algorithms. Indicator 1.d.1 Essential Identify and test a working search algorithm on a very large dataset Identify and test a working search algorithm (e.g., A-star search) on a very large dataset, measuring performance in terms of runtime and memory usage.

5 .. Indicator 1.e.1 Expected Orally present the results of an investigation Orally present the results.. Standard 2 Essential UNDERSTAND THAT PROBLEM-SCALING REQUIRES IMPROVED HEURISTICS The student will investigate and understand that a very large-scale problem requires the identification and use of improved heuristics for efficient search, even when the underlying search algorithm has already been developed in an optimal manner. AI1.2.a,b,c: Combinatorial-type problems may grow to have a very large state space. The performance of even the most efficient search algorithms will suffer without improved heuristics. Improved heuristics are often context-specific and may or may not transfer easily between problems. Benchmark 2.a Essential Investigate and Understand the Relative Benefits of Different Heuristics The student will investigate and understand the relative benefits of different heuristics. Indicator 2.a.1 Essential Identify and implement different heuristics Identify and implement different heuristics for a potentially large-scale combinatorial-type problem, whether or not the actual search algorithm is applied or not, so as to compare the relative difficulty in calculating each heuristic and the corresponding gain in information and its benefit to the informed search. Indicator 2.b.1 Expected Implement a large-scale search using an improved heuristic calculation Implement a large-scale search using an improved heuristic calculation for a problem in which either a blind search or a less-refined heuristic would be insufficient... Standard 3 Essential UNDERSTAND LOCAL SEARCH TECHNIQUES FOR OPTIMIZATION The student will investigate and understand that local search algorithms may offer an attractive alternative for certain classes or cases of otherwise difficult optimization problems. AI1.3.a,b,c: Nearest-neighbor considerations may be used exclusively in solving an optimization problem. Local searches may result in no solution or a sub-optimal solution. Local searches may need to be re-run if preliminary results are insufficient. AI1.3.d,e,f: Randomness may be used to improved the performance of a local search. Parallel techniques may be used to improve the likelihood of solution convergence. Evolutionary concepts may be used to solve an optimization problem. Benchmark 3.a Essential Investigate and Understand the Use of a Local Search Algorithm The student will investigate and understand the use of a local search algorithm. Indicator 3.a.1 Essential Implement a local search algorithm and quantify its performance Implement a local search algorithm such as hill climbing and use random re-start to recover from failed convergence; measure its performance: quantify the number of re-starts required as the size of the optimization problem is increased.

6 Indicator 3.a.2 Essential Implement a stochastic local search algorithm and quantify its performance Implement a stochastic local search algorithm such as first choice and use random re-start to recover from failed convergence; measure its performance: quantify the number of re-starts required as the size of the optimization problem is increased. Indicator 3.a.3 Essential Implement a parallel local search algorithm and quantify its performance Implement a parallel local search algorithm such as stochastic beam and use random re-start to recover from failed convergence; measure its performance: quantify the number of re-starts required as the size of the optimization problem is increased. Benchmark 3.b Essential Investigate and Understand the Use of a Genetic Algorithm The student will investigate and understand the use of a genetic algorithm. Indicator 3.b.1 Essential Implement a genetic algorithm and quantify its performance Implement a genetic algorithm and measure its performance: quantify the number of evolutionary steps required for convergence, based on the mutation rate, crossover technique, fitness function, and the overall population size... Standard 4 Essential UNDERSTAND RECURSIVE SEARCH APPLIED TO A GAME TREE The student will investigate and understand that recursive search may be applied to a game tree in order to determine potentially optimal play of the underlying game. AI1.4.a,b,c: Play of a game may be represented as a tree structure. Each branch of the tree represents a different course of play. Turns must be taken for a game involving more than one player. AI1.4.d,e,f: Recursive search may determine optimal play for a small enough game tree. For a larger game tree, a heuristic must be established for intermediate game states. Using a heuristic, an incomplete recursive search may then determine (possibly) optimal play. Benchmark 4.a Essential Investigate and Understand the Use of a Recursive Search The student will investigate and understand the use of a recursive search on a game tree. Indicator 4.a.1 Essential Implement a recursive search for use on a game tree Implement a recursive search for use on a game tree, reading in any necessary data from a file, and also implementing any necessary human-computer interface for the actual playing of the game; results should indicate optimal play for a small enough tree which may be translated into hints during the actual game play. Benchmark 4.b Essential Investigate and Understand the Use of Heuristics and Pruning with a Recursive Search The student will investigate and understand the use of heuristics and pruning with a recursive search on a game tree. Indicator 4.b.1 Essential Identify and implement heuristics for a recursive search for use on a game tree Identify and implement a heuristic for evaluating an intermediate state of a game, so that a recursive search may be cut-off before reaching the bottom of a very large game tree; the number of levels actually searched is called the ply. Indicator 4.b.2 Essential Implement pruning for a recursive search for use on a game tree Implement pruning so that a recursive search does not take any unnecessary branches through the game tree.