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\footnotesize \sf
Computer Science and Engineering
\\
Ferguson Hall -- 0115
\\
472-2402
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\LARGE \bf
Course Specification\vspace*{3mm}
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{\Large \bf \begin{center} CSCE-476\\
Introduction to Artificial Intelligence
\end{center} }

\paragraph{Catalogue Description:}~ \newline
476 Introduction to Artificial Intelligence (3 cr).
 Prereq: CSCE
310 An introduction to basic principles, techniques, and tools now
being used in the area of machine intelligence.  Languages for AI
programming will be introduced with an emphasis on LISP.  Lecture
topics will include problem solving, search, game playing, knowledge
representation, expert systems, and applications.


\paragraph{Textbook(s) and/or Other Required Materials:}~ \newline
D. Partridge  {\em Artificial Intelligence}, Ablex Publishing Company, 1991.


\paragraph{Prerequisites by Topic:}~ 
\begin{enumerate}
\item {\bf Mastery} of mathematical maturity competence to the level of
 introductory calculus.
\item {\bf Familiarity} with basic algebra, programming, alternative
 programming styles.
\item {\bf Exposure} to Programming Languages, Recursion, Operating Systems
Services, Graphic utilities available on personal computers.
\end{enumerate}


\paragraph{Course Objectives:}~ 
\begin{enumerate}
\item {\bf Mastery} of Appreciation of the progress made toward the
 identification and construction of systems that can be considered intelligent,
 understanding of what makes a problem a candidate for AI approaches to
 problem solving and when they are better approached by conventional
 analysis based methods.  Realization of the limited view of AI that
 arises with assumption of the physical symbol hypothesis and strict
 dependence on symbolic based program construction and dependence on
 algorithmic methods that are based on exhaustive systematic but blind
 search.  The role of heuristics speeding up problem solving by
 incorporating external problem domain dependent knowledge search for
 solution paths and acceptable solutions.
\item {\bf Familiarity} with program styles for AI research and applications
 including functional, declarative, object oriented and procedural.
 Familiarity with the advantage of expert system architectures in which
 the knowledge base is not intermingled with the inference mechanism or
 engine.
 Familiarity with subsymbolic and connectionist architectures and neural
 network methods for getting solutions to problems that are not analytic
 or for which a suitable model based approach is not known.
\end{enumerate}

\vfill
{\small ~ \hfill  \today}
\newpage
\paragraph{Topics Covered:}~ \newline
History: Physical Symbol Hypothesis \\
Search Approaches: random, depth-first, breadth-first, beam search,
best-first, alpha-beta, branch and bound, A*, Dynamic programming
Expert Systems \\
Rule based systems with crisp or fuzzy logic, methods of accommodating
uncertainty, Conflict resolution,  CYC, MYCIN, Lenats AM \\
Learning: Samuel's Checker program, Networks that play backgammon, Chess
players, weight updating in neural networks. \\
Pattern Recognition:. coding, transformation and segmentation \\
Natural Language Processing: signal representation, syntax and parsing,
semantics, pragmatics, prosodics, natural language generation \\
Vision:.digitizaation, low-level processing,,segmentation methods,
Recovering 3-D information, active vision, object recognition, scene
understanding \\
Reasoning:  logical basis, assumption-based truth maintenence, monotonic
verses nonmonotonic,  approximate rule-based, fuzzy rule-based,
antecedent and consequent patterns, rule firing \\
Probability based systems:  Certainty factors, Influence diagrams,
Baye's rule \\
Knowledge Representation:  Frames, Semantic Nets, Artificial neural
nets, Augmented transition matrices, Rules, Fuzzy rules, logical rules
and facts. \\
Inferencing Methods:  Deduction, Induction, Abduction, Forward chaining,
Backward chaining, Rete Procedure \\
Toy and Real World Problems:  Scalability \\
Interdisciplinary Connections:  mathematics, decision theory and
mathematical games, psychology, statistics, linguistics, biophysics and
neuroscience and philosophy \\
Robotic Systems  Intelligent agents, path finding, collision avoidance
and relocation and locomotion of autonomous wheeled and walking and
traction vehicles


\paragraph{Relationship of Course to Program Objectives:}~ \newline


\paragraph{Contribution of Course to Meeting the Professional Component:}~ \newline


\paragraph{Class/Laboratory Schedule:}~ \newline
Lecture: 45 hours = 3 hours/week for 15 weeks


\paragraph{Prepared by:}~ \newline
A.J. Surkan, Professor


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