Antonio DELLA CIOPPA | Artificial Intelligence
Antonio DELLA CIOPPA Artificial Intelligence
cod. 0622700020
ARTIFICIAL INTELLIGENCE
| 0622700020 | |
| DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA | |
| COMPUTER ENGINEERING | |
| 2014/2015 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2012 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 6 | 60 | LESSONS |
| Objectives | |
|---|---|
| GOALS THE COURSE PRESENTS THE FOUNDATIONS FOR MODELLING ARTIFICIAL AGENTS INTERACTING WITH CHANGING ENVIRONMENTS. IT ADDRESSES KNOWLEDGE REPRESENTATION, PROBLEM SOLVING STRATEGIES AND BEHAVIOURAL LEARNING. THE LECTURES ARE COMPLEMENTED BY LAB WORK FOR BUIDING AN ARTIFICIAL AGENT BY USING LOGIC PROGRAMMING LANGUGAGES. KNOWLEDGE AND UNDERSTANDING METHODOLOGIES AND TOOLS FOR MODELLING ARTIFICIAL AGENTS. UNDERSTANDING THE RELATION AMONG KNOWLEDGE REPRESENTATION, PROBLEM SOLVING STRATEGIES AND LEARNING. APPLYING KNOWLEDGE AND UNDERSTANDING MAPPING METHODS AND TOOLS TO COMPLEX PROBLEMS. DESIGN AND IMPLEMENTATION OF ARTIFICIAL AGENTS. ESTIMATION OF COST AND PERFORMANCE. MAKING JUDGEMENTS SELECTION AND INTEGRATION OF MODELS AND METHODS PROPOSED IN THE LITERATURE FOR BUILDING ARTIFICIAL AGENTS. UNDERSTANDING METHODOLOGICAL AND TECHNOLOGICAL TRENDS IN APPLYING AI APPROACHES TO REAL PROBLEM SOLVING. COMMUNICATION SKILLS WORKING IN TEAM, WRITING TECHNICAL DOCUMENTATION, ORAL PRESENTATION OF TECHNICAL AND SCIENTIFIC CONTENT LEARNING SKILLS UNDERSTANDING METHODOLOGICAL INNOVATIONS AND TECHNOLOGY SOLUTIONS PROPOSED IN THE LITERATURE. |
| Prerequisites | |
|---|---|
| ELEMENTS OF LOGICS, DESIGN OF ALGORITHM AND DATA STRUCTURE |
| Contents | |
|---|---|
| INTRODUCTION (LECTURE: 3H) FUNDAMENTAL CONCEPTS: AGENT, ENVIRONMENT, PERFORMANCE - FOUNDATIONS: THEORY OF COMPUTATION - INTELLIGENT AGENT MODELS PROBLEM SOLVING (LECTURE: 9H) BLIND SEARCH STRATEGIES - INFORMED SEARCH STRATEGIES - LOCAL SEARCH AND OPTIMIZATION PROBLEMS ADVERSARIAL SEARCH (LECTURE: 6H) GAME THEORY - OPTIMAL DECISION - ALPHA-BETA PRUNING LOGICAL AGENTS (LECTURE: 6H) PROPOSITIONAL LOGIC - THEOREM PROVING - MODEL CHECKING - AGENTS BASED ON PROPOSITIONAL LOGIC FIRST ORDER LOGIC (LECTURE: 6H) SYNTAX AND SEMANTICS - USING FIRST ORDER LOGIC - KNOWLEDGE ENGINEERING AND FIRST ORDER LOGIC INFERENCE IN FIRST ORDER LOGIC (LECTURE: 6H) PROPOSITIONAL VS FIRST ORDER INFERENCE - UNIFICATION AND LIFTING - FORWARD AND BACKWARD CHAINING - RESOLUTION PROLOG (LECTURE: 6H; LAB: 15H) SYNTAX - HORN CLAUSES - UNIFICATION - RECURSION - BACKTRACKING |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES LECTURES AND PRACTICE. DURING PRACTICE, STUDENT TEAMS ARE ASSIGNED A PROJECT THAT INCLUDES INTO A UNITARY FRAMEWORK ALL THE TOPICS OF THE COURSE, AND IS INSTRUMENTAL FOR PROVIDING THE STUDENT WITH THE SKILL FOR APPLYING THE ACQUIRED KNOWLEDGE AND WORKING IN TEAM. |
| Verification of learning | |
|---|---|
| THE FINAL EVALUATION IS BY ORAL EXAMINATION ON THE CONTENTS OF THE COURSE AND PRESENTATION OF THE PROJECT. THE GRADE IS THE WEIGHTED SUM OF PROJECT CONTENT (70%), PROJECT PRESENTATION (10%) AND ORAL EXAMINATION (20%) |
| Texts | |
|---|---|
| S. RUSSELL, P. NORVIG, ARTIFICIAL INTELLIGENCE: A MODERN APPROACH, VOLUME I, PEARSON, 3 ED., 2009 L. STERLING, E. SHAPIRO, THE ART OF PROLOG, SECOND EDITION: ADVANCED PROGRAMMING TECHNIQUES, THE MIT PRESS, 1986 |
| More Information | |
|---|---|
| ADDITIONAL MATERIAL IS AVAILABLE ON THE COURSE WEBSITE |
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