Computer Science 456: Artificial Intelligence and Expert Systems
Unit 1: Artificial Intelligence and Predicate Calculus
Artificial intelligence (AI) is a branch of computing science that is concerned with developing ideas for automating intelligent behavior and making computers perform tasks that require reasoning. In this unit, you will learn about the roots of AI and get an overview of different application domains. This unit will also present the predicate calculus, which is a well defined language widely used by AI practitioners for describing and reasoning about qualitative aspects of systems.
Activities
Familiarize yourself with the textbook resources website by navigating to the student resources at http://www.pearsonhighered.com/educator/product/Artificial-Intelligence-Structures-and-Strategies-for-Complex-Problem-Solving/9780321545893.page.
Section 1.1: Artificial Intelligence Roots and Applications
This section presents a brief history of AI foundations and discusses the computational models as well as the biological and social models of intelligence. It also discusses different areas of AI application. Some foundational concepts of AI such as the Turing test, knowledge representation, and reasoning are introduced.
Learning Objectives
- Define artificial intelligence.
- Discuss AI models and areas of application.
- Introduce some foundational of concepts of AI.
Key Terms
artificial intelligence, Turing test, autonomous agents, automated reasoning, expert systems, machine learning
Readings
Read Chapter 1, AI: History and Applications, in the textbook: George F. Luger (2008). Artificial Intelligence. Structures and Strategies for Complex Problem Solving, 6th edition. Addison Wesley.
Tasks
Practice the following exercises from Chapter 1 of the textbook:
- Exercises 4, 5, 6, and 7. You may want to use the Personal Workspace wiki on the course home page and/or share your observations with classmates in the COMP 456 General Discussion forum.
Section 1.2: Propositional and Predicate Calculus
Artificial intelligence in general deals with problems that involve qualitative problem solving, reasoning, and organizing large amount of knowledge. Hence the need for a representation language that presents a well-defined formal semantics and complete inference rules. This section presents the propositional and predicate calculus adopted as the representation language for AI since its early stages.
Learning Objectives
- Define the syntax and semantics of propositional calculus.
- Discuss the inference rules of predicate calculus.
- Illustrate the use of predicate calculus in problem solving.
- Outline the underlying theory for the concepts of inference rules and unification.
Key Terms
propositional calculus, predicate calculus, atomic sentence, interpretation, inference rules, proof procedure, modus ponens, modus tollens, unification
Tasks
Practice the following exercises from Chapter 2 of the textbook:
- Exercises 2, 5, 6, and 10. You may want to use the Personal Workspace wiki on the course home page and/or share your observations with classmates in the COMP 456 General Discussion forum.