Students who complete this unit will demonstrate that they can:

• Describe and explain the search paradigm for problem solving.
• Formulate an efficient problem space for a problem expressed in natural language in terms of initial and goal states, and operators.
• Describe the role of heuristics and describe the trade-offs among completeness, optimality, time complexity, and space complexity.
• Compare and contrast the standard AI search algorithms.
• Describe and explain the local search paradigm for problem solving.
• Compare and contrast the local and global search algorithms.
• Describe the problem of combinatorial explosion of search space and its consequences.
• Compare and contrast genetic algorithms with classic search techniques.
• Compare and contrast various heuristic searches vis-a-vis applicability to a given problem
• Design and implement a hill-climbing search algorithm.
• Design and implement a simulated annealing schedule to avoid local minima in a problem.