Explanation in artificial intelligence: Insights from the social sciences

http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution, and application-level multicast applications. These P2P overlay networks attempt to provide a long list of features, such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing, and explicit notion of locality. In this article we present a survey and comparison of various Structured and Unstructured P2P overlay networks. We categorize the various schemes into these two groups in the design spectrum, and discuss the application-level network performance of each group.

/pdf/a-survey-and-comparison-of-peer-to-peer-overlay-network-cpcv2ij1hk.pdf

A survey and comparison of peer-to-peer overlay network schemes

Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in a given partial order. We consider the problem of discovering frequently occurring episodes in a sequence. Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and present detailed experimental results. The methods are in use in telecommunication alarm management.

/pdf/discovery-of-frequent-episodes-in-event-sequences-2yphu9jeeq.pdf

Discovery of Frequent Episodes in Event Sequences

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.

The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.

The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.

The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.

- Covers the whole field of constraint programming
- Survey-style chapters
- Five chapters on applications

Table of Contents

Foreword (Ugo Montanari) 
Part I : Foundations
Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) 
Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) 
Chapter 3. Constraint Propagation (Christian Bessiere) 
Chapter 4. Backtracking Search Algorithms (Peter van Beek) 
Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) 
Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel)
Chapter 7. Tractable Structures for CSPs (Rina Dechter)
Chapter 8. The Complexity of Constraint Languages
(David Cohen, Peter Jeavons) 
Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) 
Chapter 10. Symmetry in Constraint Programming
(Ian P. Gent, Karen E. Petrie, Jean-Francois Puget)
Chapter 11. Modelling (Barbara M. Smith) 
Part II : Extensions, Languages, and Applications

Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) 
Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte)
Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) 
Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) 
Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) 
Chapter 17. Constraints over Structured Domains
(Carmen Gervet) 
Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh)
Chapter 19. Temporal CSPs (Manolis Koubarakis)
Chapter 20. Distributed Constraint Programming
(Boi Faltings) 
Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel)
Chapter 22. Constraint-Based Scheduling and Planning
(Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten)
Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) 
Chapter 24. Configuration (Ulrich Junker)
Chapter 25. Constraint Applications in Networks
(Helmut Simonis)
Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

/pdf/handbook-of-constraint-programming-53srutguo2.pdf

Handbook of Constraint Programming

When planning systems deal with realistic domains, they must cope with a large variety of constraints imposed by the environment such as temporal or resource constraints The robustness of the generated plan is a direct consequence of a correct handling of these constraints We argue that increasing the expressiveness of a representation can be achieved without fundamentally affecting the global efficiency of the search This paper presents a temporal planner, LxTeT, which integrates sharable resource management into the process of plan generation In LxTeT, planning operators are described as temporal structures of conditions, effects and sharable resource uses During the search, pending subgoals, protection threats and resource conflicts are detected by three flaw analysis modules The detection of sharable resource conflicts is performed thanks to an efficient clique-search algorithm on a possible intersection graph The control of the search is based on a least-commitment opportunistic strategy Our approach has been implemented, tested and shown to be satisfactory in various application domains.

/pdf/planning-with-sharable-resource-constraints-3i3dqh4x43.pdf

Planning with sharable resource constraints

https://hal.archives-ouvertes.fr/hal-01137921/document

Deliberation for autonomous robots: A survey

The situation recognition system, to which this paper is devoted, receives as input a stream of time-stamped events; it performs recognition of instances of occurring situations, as they are developing, and it generates as output deduced events and actions to trigger. It is mainly a temporal reasoning system. It is predictive in the sense that it predicts forthcoming events relevant to its task, it focuses its attention on them and it maintains their temporal windows of relevance. Its main functionality is to recognize efficiently complex temporal patterns on the fly, while they are taking place. This system has been tested for the surveillance of an environment by a multisensory perception machine; it is being applied to monitoring a complex dynamic system.

https://ijcai.org/Proceedings/93-1/Papers/024.pdf

Situation recognition: representation and algorithms

On chronicles: representation, on-line recognition and learning

Publisher Summary
This chapter builds on temporal reasoning techniques for planning by presenting some adequate representations for planning involving explicit time and by developing two related approaches to temporal planning. Time is dealt with mainly within point algebra (PA) and the simple temporal networks. Other approaches that rely on interval algebra (IA) are discussed. The chapter focuses on temporal planners that extend certain planning techniques. However, it departs significantly from the model of state-transition systems. It views an action not as a single state transition but as a collection of local change and persistence conditions that are spread out in time but are focused on just a few state variables or propositions. Such a view offers several advantages in expressiveness. In particular, explicit time is essential for handling the interaction of concurrent actions properly.

Malik Ghallab

Papers

Planning with sharable resource constraints

Deliberation for autonomous robots: A survey

Situation recognition: representation and algorithms

On chronicles: representation, on-line recognition and learning

Chapter 14 – Temporal Planning