CSEN 901 Introduction to Artificial Intelligence

Course Information

Abstract

  • CSEN901 is an introductory course in artificial intelligence (AI). The definition of AI is itself rather controversial;
    but, as a starting point, we may think of AI as the study of how to program computers to behave in ways which, when
    observed in humans, is usually attributed to "intelligence". The course starts with an attempt to identify exactly what
    we mean by "intelligence". The history and philosophical foundations of AI are then glossed over. Since most AI problems
    could be conceptualized as search problems, an intensive study of search algorithms comes next. Knowledge-based AI systems
    are complex mutations of the simple search-based systems, with the complexity emerging from the elaborate representations
    of world states and operators. Logic is the main tool employed in knowledge-based systems; a study of logic and reasoning
    (mainly resolution) is hence necessary. By thus laying the foundations of AI, a number of topics are then introduced:
    planning, learning, and plan execution. All along, an agent-oriented approach is taken to motivate the various topics.
     

Outline

    1. History and philosophical foundations of AI.
    2. Intelligent agents
    3. Problem solving by searching
    4. Informed search methods
    5. Propositional and predicate logic
    6. Resolution
    7. Logical reasoning systems
    8. Planning
    9. Planning and acting
    10. Learning
       

Objectives

  • After passing this course, students should be able to do the following:

    1-Describe the Turing test for machine intelligence.
    2-Idetify different types of agents.
    3-Determine the type of agent sufficient for a particular task.
    4-Model problems as searh problems.
    5-Apply various search strategies.
    6-Analyse the time and space compelxities of different search strategies.
    7-Prove completeness and optimality results for various search strategies.
    8-Translate English sentences into propositional and first-order logic.
    9-Construct derivations using natural deduction systems.
    10-Construct derivations using generalized modus ponens.
    11-Construct derivations using resolution refutation.
    12-Identify Horn sentences.
    13-Convert a first-order WFF into CNF, INF, and clause form.
    14-Unify two expressions.
    15-Model planning problems using STRIPS operators.
    16-Construct a partial-order plan.
    17-Construct a partial-order conditional plans.
    18-Identify the result of replanning while executing a plan.
    19-Construct a decision tree from a given set of example.
    20-Apply current-best-hypothesis search for learning.
    21-Apply version-space search for learning.

Textbooks

  • Artificial Intelligence: A Modern Approach (2nd edition)
    Stuart Russell and Peter Norvig.
    Prentice Hall
    ISBN 0137903952

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