ing Constraints Using Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 972 James Gibbons Sensitivity Analysis in CSPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973 Diarmuid Glynn Solution Stability in Constraint Satisfaction Problems . . . . . . . . . . . . . . . . . . 974 Emmanuel Hebrard distn: An Euclidean Distance Global Constraint . . . . . . . . . . . . . . . . . . . . . . . 975 Michael Heusch Algorithmic Mechanism Design and Constraints . . . . . . . . . . . . . . . . . . . . . . . 976 Alan Holland and Barry O’Sullivan XVIII Table of

Principles and Practice of Constraint Programming – CP 2003

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/0583_2005-SC-Lozano.pdf

Replacement strategies to preserve useful diversity in steady-state genetic algorithms

This collection represents the primary reference work for researchers and students in the area of Temporal Reasoning in Artificial Intelligence Temporal reasoning has a vital role to play in many areas, particularly Artificial Intelligence Yet, until now, there has been no single volume collecting together the breadth of work in this area This collection brings together the leading researchers in a range of relevant areas and provides an coherent description of the breadth of activity concerning temporal reasoning in the filed of Artificial Intelligence

Key Features:

- Broad range: foundations; techniques and applications
- Leading researchers around the world have written the chapters
- Covers many vital applications
- Source book for Artificial Intelligence, temporal reasoning
- Approaches provide foundation for many future software systems

·Broad range: foundations; techniques and applications
·Leading researchers around the world have written the chapters
·Covers many vital applications
·Source book for Artificial Intelligence, temporal reasoning
·Approaches provide foundation for many future software systems

Table of Contents

"Formal Theories of Time and Temproal Incidence", Lluis Vila 

"Eventualities", Antony Galton 

"Time Granularity", Jerome Euzenat and Angelo Montanari 

"Modal Varieties of Temporal Logic", Howard Barringer and Dov Gabbay 

"Temporal Qualification in Artificial Intelligence", Han Reichgelt and Lluis Vila 

"Computational Complexity of Temporal Constraint Problems", Thomas Drakengren and Peter Jonsson 

"Indefinite Constraint Databases with Temporal Information: Representational Power and Computational Complexity", Manolis Koubarakis 

"Processing Qualitative Temporal Constraints", Alfonso Gerevini 

"Theorem-Proving for Discrete Temporal Logic", Mark Reynolds/Clare Dixon 

"Probabilistic Temporal Reasoning", Steve Hanks/David Madigan 

"Temporal Reasoning with iff-Abduction", Marc Denecker/Kristof Van Belleghem 

"Temporal Description Logics", Alessandro Artale/Enrico Franconi 

"Logic Programming and Reasoning about Actions", Chitta Baral/Michael Gelfond 

"Temporal Databases" Jan Chomicki/David Toman 

"Temporal Reasoning in Agent-Based Systems" Michael Fisher/Michael Wooldridge 

"Time in Planning" Maris Fox/Derek Long 

"Time in Automated Legal Reasoning" Lluis Vila/Hajime Yoshino 

"Temporal Reasoning in Natural Language" Alice ter Meulen 

"Temporal Reasoning in Medicine" Elpida Keravnou/Yuval Shahar 

"Time in Qualitative Simulation" Dan Clancy/Benjamin Kuipers

Handbook of Temporal Reasoning in Artificial Intelligence

The Constraint-Based Agent (CBA) framework is a set of tools for designing, simulating, building, verifying, optimizing, learning and debugging controllers for agents embedded in an active environment. The agent and the environment are modelled symmetrically as, possibly hybrid, dynamical systems in Constraint Nets, as developed by Zhang and Mackworth. This paper is a tutorial overview of the development and application of the CBA framework, emphasizing the important special case where the agent is an online constraint-satisfying device. Here it is often possible to verify complex agents as obeying real-time temporal constraint specifications and, sometimes, to synthesize controllers automatically. The CBA framework demonstrates the power of viewing constraint programming as the creation of online constraint-solvers in dynamic environments.

Principles and Practice of Constraint Programming – CP 2000

http://www.cs.ucy.ac.cy/~elpida/docs/ResearchDirectionsTemporalReasoning.pdf

Position paper: Temporal representation and reasoning in medicine: Research directions and challenges

This paper presents a comparison of two genetic algorithms (GAs) for constrained ordering problems. The first GA uses the standard selection strategy of roulette wheel selection and generational replacement (STDS), while the second GA uses an intermediate selection strategy in addition to STDS. This intermediate selection strategy keeps only the superior offspring and replaces the inferior offspring with the superior parent. We call this selection strategy i>Keep-Best Reproduction (KBR). The effect of recombination alone, mutation alone and both together are studied. We compare the performance of the different selection strategies and discuss the environment that each selection strategy needs to flourish in. Overall, KBR is found to be the selection strategy of choice. We also present empirical evidence that suggests that KBR is more robust than STDS with regard to operator probabilities and works well with smaller population sizes.

Keep-Best Reproduction: A Local Family Competition Selection Strategy and the Environment it Flourishes in

This paper presents theoretical convergence characteristics of Keep-Best Reproduction (KBR), a selection strategy for genetic algorithms (GAS). KBR was previously introduced and encouraging results were reported in the traveling salesman domain [16, 181 where KBR was compared with the standard replacement strategy of replacing the two parents by their two children. Here we demonstrate that in a non-operator environment as well as in the ONEMAX domain KBR has the same convergence characteristics as P-tournament selection and elitist recombination (ELR) [13]. We also show how a modification of ELR suggested in [15] can be utilized to tune the selection pressure of KBR. These analytical models are fairly simplistic and cannot accurately model the convergence characteristics in more complex domains where building blocks are correlated, such as the TSP domain. We will give some empirical results of a comparison of KBR and ELR in this domain.

Convergence characteristics of keep-best reproduction

Genetic Algorithms (GAs) have traditionally been designed to work on bitstrings. More recently interest has shifted to the application of GAs to constraint optimization and combinatorial optimization problems. Important for an effective and efficient search is the use of a suitable crossover operator. This paper analyses the performance of six existing crossover operators in the traveling salesman domain. While the edge recombination operator was reported to be the most suitable operator in the TSP domain, our results suggest that this is only true for symmetric TSPs. The problem with edge recombination is that it inverts edges found in the parents. This has no negative effect for the symmetric TSP but can have a substantial effect if the TSP is asymmetric. We propose an edge based crossover operator for the asymmetric TSP and demonstrate its superiority over the traditional edge recombination. Another interesting finding is that order crossover (OX) which has an average performance for symmetric problems, performs very well on asymmetric problems.

ASERC - A Genetic Sequencing Operator for Asymmetric Permutation Problems

This paper presents a comparison of two genetic algorithms (GAs) that use different selection strategies. The first GA uses the standard selection strategy of roulette wheel selection and generational replacement (STDS), while the second GA uses an intermediate selection strategy in addition to STDS. Our previous research has shown that this intermediate selection strategy, which we call “Keep-Best Reproduction (KBR)”, found solutions of lower cost for a variety of travelling salesman problems. In this paper, we study the effects of crossover and mutation probabilities on STDS as well as on KBR. We study the effect of recombination alone, mutation alone and both together. We compare the performance of the different selection strategies and discuss the environment that each selection strategy needs to flourish in. Overall, KBR is found to be the selection strategy of choice. We also present empirical evidence that suggests that KBR is more robust than STDS with regard to operator probabilities.

The Effect of Genetic Operator Probabilities and Selection Strategies on the Performance of a Genetic Algorithm

Probabilistic temporal representation and reasoning

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