Introduction to Artificial Intelligence

Semester A 2022-2023 (Fall 2022)

* Under construction *

BGU Computer Science Department

Description of the course

Artificial intelligence (AI) has recently regained the limelight, as the human world chess champion was beaten by Deep Blue, a program written by a team of researchers and programmers from IBM. Even more recently, a "re-match" against a distributed machine in Jerusalem also favored a computer program. In the more difficult field of partial information and chance games, such as Poker, AI programs now hold their own against human champions, as exhibited in a competition held during AAAI-2008. Just a few years ago (2016), a computer GO program (Alpha-Go) achieved a major breakthrough by attaining championship level in game play using deep learning and Monte-Carlo tree search.

In the even more difficult field of automated robotics, a nearly fully autonomous car has recently (2012) been successful in road tests in the Western USA (Google driverless car). Commercialization of the autonomous vehicle is getting nearer as well through the Tesla car and others, though there is still a significant path to go until full autonomy and safety is achieved (Tesla-Mobileye mishap 2016). Other true AI applications are also on the rise, from expert systems for diagnosis and advice, through increasingly intelligent robots, to intelligent and autonomous www agents.

This course deals with the issues of defining intelligence and rationality in an agent, various methods of formalizing them, and models for representing and using knowledge. In specific topics, mainly search, logical reasoning, probabilistic reasoning, and decision-making under uncertainty, the course will focus all the way down to the algorithm level, in order to provide some hands-on experience with programming artificially intelligent agents. We will also briefly examine the currently hot topic of meta-reasoning in search.

This course is now on MOODLE, so see there!

Course data and information pointers

  • Environment simulator pseducode from Russell and Norvig.
  • Some additional definitions and algorithms for Bayes networks.
  • Belief propagation in chain BNs.
  • Additional algorithms for NN learning.
  • Example quiz and Answers
  • Lecture topics and notes.

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