Constraint Programming with Intelligent Backtracking using Artificial Intelligence

2022 ◽  
Author(s):  
Wen Ma ◽  
Zuyuan Huang ◽  
Ching-Hsien Hsu ◽  
Carlos Enrique Montenegro-Marin
Keyword(s):  
Artificial Intelligence ◽  
Constraint Programming ◽  
Intelligent Backtracking

Multi-Vehicle Missions

2008 ◽  
pp. 1-22
Author(s):  
Johan Baltié ◽  
Eric Bensana ◽  
Patrick Fabiani ◽  
Jean-Loup Farges ◽  
Stéphane Millet ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Constraint Programming ◽  
Management System ◽  
Experimental Results ◽  
Application Domain ◽  
Research Directions ◽  
Air Defense ◽  
Mission Management ◽  
Unmanned Combat Air Vehicles ◽  
Air Vehicles

This chapter deals with the issues associated with the autonomy of vehicle fleets, as well as some of the dimensions provided by an Artificial Intelligence (AI) solution. This presentation is developed using the example of a suppression of enemy air defense mission carried out by a group of Unmanned Combat Air Vehicles (UCAV). The environment of the Mission Management System (MMS) includes the theatre of operations, vehicle sub-systems and the MMS of other UCAV. An MMS architecture, organized around a database, including reactive and deliberative layers is described in detail. The deliberative layer includes a distributed mission planner developed using constraint programming and an agent framework. Experimental results demonstrate that the MMS is able, in a bounded time, to carry out missions, to activate the contingent behaviors, to decide whether to plan or not. Some research directions remain open in this application domain of AI.

scholarly journals Introduction to the special volume on constraint programming, artificial intelligence, and operations research

2008 ◽  
Vol 171 (1) ◽  
pp. 1-2 ◽  
Author(s):  
J. Christopher Beck ◽  
Barbara M. Smith
Keyword(s):  
Artificial Intelligence ◽  
Constraint Programming ◽  
Operations Research ◽  
Special Volume

Explainable Artificial Intelligence. Model Discovery with Constraint Programming

2021 ◽  
pp. 171-191
Author(s):  
Antoni Ligęza ◽  
Paweł Jemioło ◽  
Weronika T. Adrian ◽  
Mateusz Ślażyński ◽  
Marek Adrian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Constraint Programming ◽  
Explainable Artificial Intelligence

Combining satisfiability techniques from AI and OR

2000 ◽  
Vol 15 (1) ◽  
pp. 31-45 ◽  
Author(s):  
HEIDI E. DIXON ◽  
MATTHEW L. GINSBERG
Keyword(s):  
Artificial Intelligence ◽  
Operations Research ◽  
Cutting Plane ◽  
Intelligence Community ◽  
Recent Effort ◽  
Constraint Set ◽  
Intelligent Backtracking ◽  
Satisfiability Algorithms ◽  
Resolution Proof ◽  
Theory Lead

The recent effort to integrate techniques from the fields of artificial intelligence and operations research has been motivated in part by the fact that scientists in each group are often unacquainted with recent (and not so recent) progress in the other field. Our goal in this paper is to introduce the artificial intelligence community to pseudo-Boolean representation and cutting plane proofs, and to introduce the operations research community to restricted learning methods such as relevance-bounded learning. Complete methods for solving satisfiability problems are necessarily bounded from below by the length of the shortest proof of unsatisfiability; the fact that cutting plane proofs of unsatisfiability can be exponentially shorter than the shortest resolution proof can thus in theory lead to substantial improvements in the performance of complete satisfiability engines. Relevance-bounded learning is a method for bounding the size of a learned constraint set. It is currently the best artificial intelligence strategy for deciding which learned constraints to retain and which to discard. We believe these two elements or some analogous form of them are necessary ingredients to improving the performance of satisfiability algorithms generally. We also present a new cutting plane proof of the pigeonhole principle that is of size n2, and show how to implement some intelligent backtracking techniques using pseudo-Boolean representation.

scholarly journals AI Methods in Algorithmic Composition: A Comprehensive Survey

2013 ◽  
Vol 48 ◽  
pp. 513-582 ◽  
Author(s):  
J.D. Fernandez ◽  
F. Vico
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Constraint Programming ◽  
Music Composition ◽  
Probabilistic Methods ◽  
Computational Techniques ◽  
Algorithmic Composition ◽  
Rule Based ◽  
Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

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