The first module of the course covers three parts: Introduction to Artificial Intelligence, Intelligent Agents, and Searching. The first two parts are fairly straightforward. The focus of this module will be on the search strategies. Searching is one of the foundation research topics in Computer Science and has a wide usage in many applications. Through textbook reading I wish you obtain a basic idea on several important search strategies: breadth-first search, depth-first search, and A* search. A* is a generic search strategy in which possible solutions are compared using an evaluation function f(n) = g(n) + h(n) - here g(n) is the cost so far to reach n, h(n) is the estimated cost from n to goal, and f(n) is estimated total cost of path through n to a goal state. This strategy is guaranteed for find the optimal solution but the searching cost is limited by the design of the heuristic function h(n). The quality of a heuristic can usually be shown by how well it is to limit the search space - and finally reduce the computational cost. Please note that the heuristic is one fundamental technique in AI. From one perspective, AI is about the design of heuristics for complex problems, in my opinion. The design of heuristics often involves the use of domain knowledge in a specific application area, and its design goals include the reduction of computational cost and the improvement of performances. Some important measures (e.g., admissibility, Monotonicity, and Informedness) for comparing search algorithms are also presented through the discussion of A*.
You may want to refer to some of the presentation slides I used before for better understanding on the course contents. Please note that the slides document covers many candidate topics for the course, which may or may not be the focus during this term.
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I will post topics for discussion from time to time. I wish to see your active participation in these discussions. You can also start your own discussion threads as long as it is related to course materials. As you can see from the course syllabus, online participation is an important part of your final grade.
Let's start from the categorization of intelligent agents (the word agent has been used in many disciplines and I don't think there is a consensus on its definition). According to the AI textbook, how many different agents can be found? Can you find a real-world example/application for each of them?
When you cite external materials beyond the textbook, please show your reference at the end of your posting so that other readers can get more an in-depth knowledge in case they want to explore further. This also applies to other discussion threads.