Computer science | ELEMENTS OF ARTIFICIAL INTELLIGENCE

cod. 0512100031

ELEMENTS OF ARTIFICIAL INTELLIGENCE

	0512100031
	DIPARTIMENTO DI INFORMATICA

	COMPUTER SCIENCE
	2013/2014

CURRICULUM METODOLOGICO Cohort 2012/2013

	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2008
	PRIMO SEMESTRE

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
	INF/01	3	24	LESSONS	OPTIONAL SUBJECTS

	Objectives
	THE COURSE PROVIDES A CONCISE INTRODUCTION TO A.I. FOR STUDENTS WITH NO PRIOR KNOWLEDGE OF THE TOPIC AND PREPARES THE STUDENT FOR A MORE IN-DEPTH STUDY OF THIS SUBJECT. MORE SPECIFICALLY: KNOWLEDGE AND UNDERSTANDING: INEVITABLY CERTAIN TOPICS ARE OMITTED, HOWEVER THE CORE TOPICS ARE ADEQUATELY TREATED (KNOWLEDGE REPRESENTATION AND INFERENCE, EXPERT SYSTEMS, USING SEARCH IN PROBLEM SOLVING, GAME THEORY, REASONING UNDER UNCERTAINTY, NATURAL LANGUAGE PROCESSING). APPLYING KNOWLEDGE AND UNDERSTANDING: PROBLEM SOLVING ABILITIES APPLIED IN CONCEIVING, DESIGNING AND IMPLEMENTING A SIMPLE EXPERT SYSTEM. MAKING JUDGMENTS: ABILITY TO IDENTIFY THE APPROPRIATE KNOWLEDGE REPRESENTATION LANGUAGE TO DEAL WITH A SPECIFIC SIMPLE PROBLEM. CAPABILITY OF CRITICAL ANALYSIS ABOUT THE COMPLEXITY OF SIMPLE PROBLEMS. COMMUNICATION SKILLS: ABILITY TO COMMUNICATE INFORMATION, IDEAS, PROBLEMS, RATIONALE REGARDING SIMPLE A.I. PROBLEMS LEARNING SKILLS: DEVELOPMENT OF SKILLS SO THAT FURTHER STUDY ABOUT A.I. TECHNIQUES MAY BE PARTIALLY SELF-DIRECTED. NO SPECIFIC PROGRAMMING LANGUAGE IS ASSUMED AND THE ALGORITHMS ILLUSTRATED CAN BE IMPLEMENTED IN DIFFERENT LANGUAGES.

THE COURSE PROVIDES A CONCISE INTRODUCTION TO A.I. FOR STUDENTS WITH NO PRIOR KNOWLEDGE OF THE TOPIC AND PREPARES THE STUDENT FOR A MORE IN-DEPTH STUDY OF THIS SUBJECT. MORE SPECIFICALLY:

KNOWLEDGE AND UNDERSTANDING:
INEVITABLY CERTAIN TOPICS ARE OMITTED, HOWEVER THE CORE TOPICS ARE ADEQUATELY TREATED (KNOWLEDGE REPRESENTATION AND INFERENCE, EXPERT SYSTEMS, USING SEARCH IN PROBLEM SOLVING, GAME THEORY, REASONING UNDER UNCERTAINTY, NATURAL LANGUAGE PROCESSING).

APPLYING KNOWLEDGE AND UNDERSTANDING:
PROBLEM SOLVING ABILITIES APPLIED IN CONCEIVING, DESIGNING AND IMPLEMENTING A SIMPLE EXPERT SYSTEM.

MAKING JUDGMENTS:
ABILITY TO IDENTIFY THE APPROPRIATE KNOWLEDGE REPRESENTATION LANGUAGE TO DEAL WITH A SPECIFIC SIMPLE PROBLEM. CAPABILITY OF CRITICAL ANALYSIS ABOUT THE COMPLEXITY OF SIMPLE PROBLEMS.

COMMUNICATION SKILLS:
ABILITY TO COMMUNICATE INFORMATION, IDEAS, PROBLEMS, RATIONALE REGARDING SIMPLE A.I. PROBLEMS

LEARNING SKILLS:
DEVELOPMENT OF SKILLS SO THAT FURTHER STUDY ABOUT A.I. TECHNIQUES MAY BE PARTIALLY SELF-DIRECTED. NO SPECIFIC PROGRAMMING LANGUAGE IS ASSUMED AND THE ALGORITHMS ILLUSTRATED CAN BE IMPLEMENTED IN DIFFERENT LANGUAGES.

	Prerequisites
	FAMILIARITY WITH A PROGRAMMING LANGUAGE AND WITH THE BASIC CONCEPTS OF THEORY OF PROBABILITY

	Contents
	KNOWLEDGE REPRESENTATION AND INFERENCE. KNOWLEDGE REPRESENTATION LANGUAGES. SEMANTIC NETWORKS AND FRAMES. PROPOSITIONAL LOGIC. PREDICATE LOGIC. SYNTAX, SEMANTICS, INFERENCE RULES. LOGIC PROGRAMMING. RULE-BASED SYSTEMS. FORWARD CHAINING SYSTEMS. BACKWARD CHAINING SYSTEMS. EXPERT SYSTEMS KNOWLEDGE ENGINEERING. EXPERT SYSTEM ARCHITECTURE. PROBLEM-SOLVING METHODS. BACKWARD CHAINING RULE-BASED EXPERT SYSTEMS. REASONING UNDER UNCERTAINTY THE CONCEPT OF PROBABILITY. CONDITIONAL PROBABILITY. BAYES' THEOREM AND APPLICATIONS. LIKELIHOOD RATIOS. NORMALIZATION. USING SEARCH IN PROBLEM SOLVING. GRAPHS AND TREES. BFS AND DFS ALGORITHMS. HEURISTIC SEARCH: HILL CLIMBING, BEST FIRST SEARCH, THE A* ALGORITHM. PROBLEM SOLVING AS SEARCH: PLANNING TECHNIQUES AND THE MEA APPROACH. NATURAL LANGUAGE PROCESSING. SPEECH RECOGNITION. SYNTACTIC ANALYSIS: GRAMMARS, PARSING. SEMANTIC ANALYSIS. PRAGMATICS. AMBIGUITY IN LANGUAGE. LANGUAGE GENERATION.

Contents

KNOWLEDGE REPRESENTATION AND INFERENCE.
KNOWLEDGE REPRESENTATION LANGUAGES. SEMANTIC NETWORKS AND FRAMES. PROPOSITIONAL LOGIC. PREDICATE LOGIC. SYNTAX, SEMANTICS, INFERENCE RULES. LOGIC PROGRAMMING. RULE-BASED SYSTEMS. FORWARD CHAINING SYSTEMS. BACKWARD CHAINING SYSTEMS.
EXPERT SYSTEMS
KNOWLEDGE ENGINEERING. EXPERT SYSTEM ARCHITECTURE. PROBLEM-SOLVING METHODS. BACKWARD CHAINING RULE-BASED EXPERT SYSTEMS.

REASONING UNDER UNCERTAINTY
THE CONCEPT OF PROBABILITY. CONDITIONAL PROBABILITY. BAYES' THEOREM AND APPLICATIONS. LIKELIHOOD RATIOS. NORMALIZATION.

USING SEARCH IN PROBLEM SOLVING.
GRAPHS AND TREES. BFS AND DFS ALGORITHMS. HEURISTIC SEARCH: HILL CLIMBING, BEST FIRST SEARCH, THE A* ALGORITHM. PROBLEM SOLVING AS SEARCH: PLANNING TECHNIQUES AND THE MEA APPROACH.

NATURAL LANGUAGE PROCESSING.
SPEECH RECOGNITION. SYNTACTIC ANALYSIS: GRAMMARS, PARSING. SEMANTIC ANALYSIS. PRAGMATICS. AMBIGUITY IN LANGUAGE. LANGUAGE GENERATION.

	Teaching Methods
	LECTURES EXERCISES HOMEWORK ASSIGNMENTS CASE STUDIES

	Verification of learning
	THE COURSE WILL BE GRADED ON THE BASIS OF ONE COMPREHENSIVE FINAL EXAM INVOLVING A WRITTEN TEST AND AN ORAL EXAM. IN THE WRITTEN TEST THE STUDENT HAS TO DEVISE A SOLUTION FOR A SIMPLE A.I. PROBLEM. IN THE ORAL EXAM THE STUDENT HAS TO SHOW KNOWLEDGE OF ALL THE TOPICS STUDIED IN THE COURSE. ALSO PROJECT PRESENTATION WILL BE USED TO COMPUTE THE FINAL GRADE.

	Texts
	- LECTURE NOTES - CAWSEY A.: THE ESSENCE OF ARTIFICIAL INTELLIGENCE – PRENTICE HALL CAPP.1-5 ESCLUSI PARAG. 3.5, 4.3.1, CAP. 7 ESCLUSI PARAG. 7.3, 7.4 - RUSSELL S.J., NORVIG P.: INTELLIGENZA ARTIFICIALE, VOL.2 - PEARSON CAPP.13, 17.6

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