[Wiki] Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal “intelligent” machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”. As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of “artificial intelligence”, having become a routine technology. Capabilities currently classified as AI include successfully understanding human speech, competing at a high level in strategic game systems (such as Chess and Go), self-driving cars, and interpreting complex data. Some people also consider AI a danger to humanity if it progresses unabatedly. AI research is divided into subfields that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.
The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it.” This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Attempts to create artificial intelligence have experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973, the second AI winter 1987–1993and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.
After completing this course, students will be able to:
- To have an appreciation for the concept of Intelligence and Artificial Intelligence;
- Describe Artificial Intelligence concepts and architecture considerations;
- To know an understand various AI search algorithms : uninformed, informed, heuristic, constraint satisfaction;
- To have an appreciation for the engineering issues underlying the design of AI systems;
- Understand the fundamentals of Constraint Programming techniques;
- Understand the fundamentals of logic-based knowledge representation, inference and theorem proving;
- To have a basic proficiency in a traditional AI language (Prolog) including an ability to write simple to intermediate programs and an ability to understand code written in that language;
- Ability to apply knowledge representation, reasoning, and resolution techniques to real-world problems.
- Chapitre 1: Introduction to Artificial Intelligence
- Chapitre 2: AI search algorithms for problems and games : uninformed, informed, heuristic, game algorithm
- Chapitre 3: Constraints Programming
- Chapitre 4: Logic-based reasoning
- Chapitre 5: AI language :Prolog
Background in computer programming, undergraduate algorithms and data structures, and basic discrete mathematics .
The classes will be given in French by default. Slides will be in French/ English and available in PDF.
- S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd edition, 2009
- D. Poole and A. Mackworth. Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010 Available online: http://artint.info/
- R. Brachman, H. Levesque. Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.
- G. Luger. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison Wesley; 6 edition, 2008
- E. Alpaydin. Introduction to Machine Learning. MIT Press, 2nd edition, 2010
- R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998
|Session 1||Introduction to Artificial Intelligence|
|Session 1||AI search algorithms for problems and games|
|Session 2||Uninformed and informed algorithms|
|Session 3||heuristic, game algorithm|
|Session 4||Constraints Programming|
|Session 5||Choco Solver|
|Session 6||Distributed Constraints Reasoning and Multi Agents System|
|Session 7||Logic-based reasoning : Propositional logic|
|Session 8||Logic-based reasoning : Predicate logic (1/2)|
|Session 9||Logic-based reasoning : Predicate logic (2/2)|
|Session 10||AI language :Prolog (1/2)|
|Session 11||AI language :Prolog (2/2)|