Lecture

Constraint Satisfaction: Formulation and Algorithms

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In course

CS-330: Artificial intelligence

Introduction aux techniques de l'Intelligence Artificielle, complémentée par des exercices de programmation qui montrent les algorithmes et des exemples de leur application à des problèmes pratiques. ...

Description

This lecture covers the formulation of constraint satisfaction problems (CSP) and systematic algorithms for solving them. It discusses the challenges of combinatorial explosion in general search algorithms and introduces the concept of constraint satisfaction to enable more efficient methods. Examples such as resource allocation problems are used to illustrate the formulation of CSP. The lecture also explores systematic algorithms like depth-first search and heuristics like backjumping and forward checking. Various techniques for improving the efficiency of CSP solutions, such as local search algorithms like hill-climbing and simulated annealing, are presented. The importance of consistency algorithms in reducing search complexity is highlighted, along with practical applications in scheduling, planning, and design problems.

Instructor

Boi Faltings

Professor Faltings joined EPFL in 1987 as professor of Artificial Intelligence. He holds a PhD degree from the University of Illinois at Urbana-Champaign, and a diploma from the ETHZ. His research has spanned different areas of intelligent systems linked to model-based reasoning. In particular, he has contributed to qualitative spatial reasoning, case-based reasoning (especially for design problems), constraint satisfaction for design and logistics problems, multi-agent systems, and intelligent user interfaces. His current work is oriented towards multi-agent systems and social computing, using concepts of game theory, constraint optimization and machine learning. In 1999, Professor Faltings co-founded Iconomic Systems, a company that developed a new agent-based paradigm for travel e-commerce. He has since co-founded 5 other startup companies and advised several others. Prof. Faltings has published more than 150 refereed papers on his work, and participates regularly in program committees of all major conferences in the field. He has served as associate editor of of the major journals, including the Journal of Artificial Intelligence Research (JAIR) and the Artificial Intelligence Journal. From 1996 to 1998, he served as head of the computer science department.

Official source

Ontological neighbourhood

Information engineering

Artificial intelligence: Automated planning and scheduling

Related concepts (32)

Constraint satisfaction problem

Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families.

Constraint satisfaction

In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. A solution is therefore a set of values for the variables that satisfies all constraints—that is, a point in the feasible region. The techniques used in constraint satisfaction depend on the kind of constraints being considered.

Constraint programming

Constraint programming (CP) is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. In constraint programming, users declaratively state the constraints on the feasible solutions for a set of decision variables. Constraints differ from the common primitives of imperative programming languages in that they do not specify a step or sequence of steps to execute, but rather the properties of a solution to be found.

Approximation algorithm

In computer science and operations research, approximation algorithms are efficient algorithms that find approximate solutions to optimization problems (in particular NP-hard problems) with provable guarantees on the distance of the returned solution to the optimal one. Approximation algorithms naturally arise in the field of theoretical computer science as a consequence of the widely believed P ≠ NP conjecture. Under this conjecture, a wide class of optimization problems cannot be solved exactly in polynomial time.

Search algorithm

In computer science, a search algorithm is an algorithm designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with either discrete or continuous values. Although search engines use search algorithms, they belong to the study of information retrieval, not algorithmics. The appropriate search algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data.

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