Courses

This is the content of the courses I used to give at the University of Neuchâtel. For now, refer to the site of CORMAS.

Software Engineering

Artificial Intelligence I

Artificial Intelligence II

Multi-Agent Systems

Software Engineering

(second year)

Description

Introduction to the object-oriented methods of software engineering. The aim is to give to the student a systematic approach to large software programming, both individually and in a team. The main focus is on object-oriented analysis and design. Two target languages are used: C++ and Ada. This lecture is completed by projects to illustrate the system rather than only algorithmic approach.

Content

First part: software engineering
- The principles of software engineering
- The object-orientd approach
- The class-relation model
- The analysis process
- The design process
Second part: The C++ programming environment
- The C++ language
- The MVC model
- Component programming
- The frameworks
- Detailed design in C++
Third part: Ada
- The Ada language
- Some concurrency programming model
- Detailed design in Ada

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Artificial Intelligence 1

(third and fourth year)

Description

Presentation of most inference techniques in Artificial Intelligence. This course is based on CommonLisp and presents a large panorama of reasoning techniques, insisting on their generalizability and their integration in composed systems. This course is completed by exercices on concrete problems using an AI toolbox written in CLOS (closia2.tar.gz).

Content

Automatic theorem proving:
- First order predicate calculus
- Resolution principle
- Theorem proving strategies
Inference engines:
- Backward chaining: Prolog
- Forward chaining: OPS-5
- Combining both
Non-monotonic reasoning:
- Frame languages
- TMS
- ATMS
Reasoning under uncertainty:
- Bayesian methods
- Possibilist (fuzzy) reasoning
- Evidential reasoning (Dempster-Schaffer)

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Artificial Intelligence II

(third and fourth year)

Description

The content of this course covers two problem classes in AI: autonomous systems and natural language processing. In autonomous systems, we cover in terms of control theory: heuristic search, action planning, markovian decision problems and reinforcement learning. For natural language processing, we only insist on syntactic and semantical aspects. This course is completed by exercices to solve concrete problems using an AI toolbox written in CLOS (closia2.tar.gz)..

Content

Autonomous systems
- Deterministic discrete control
  - Heuristic search
  - Action planning:
    - TWEAK
    - RSTRIPS
- Stochastic discrete control
  - Control strategies
  - Adaptative control
  - Reinforcement learning
Natural language processing
- Syntax
  - Definite clause grammar
  - Extraposition grammar
  - ATN
- Montague semantics
  - Typed first-order logic
  - Typed lambda-calculus
  - Combining logic and lambda-calculus
  - Translating natural languague into logic

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Multi-agent systems

(DEA)

Description

This course introduces multi-agent systems as a field focused on the understanding of collective behaviours based on interactions between entities. Three main sources of metaphors are taken into account: biology, psychology and sociology. In effect, full understanding of collective phenomena implies at various degrees, behavioral, mental and sociological aspects. Each aspect is illustrated by concrete applications in various domains.

Content

The biological point of view
- Agent architectures
- The animal models
- Behavioural approaches
- Marquing approaches
- Pattern approaches
- Applications
The psychological point of view
- Communication and interactions
- From signs to speech acts
- The linguistic point of view
- The receiver/emitter points of view
- Specifying communication content
  - Knowledge based pragmatics
  - Action based pragmatics
  - Intention based pragmatics
  - Goal based pragmatics
The sociological point of view
- The interaction protocols
- Formalising social interactions
- Some organisations

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