Soft Concurrent Constraint Programming

Soft constraints extend classical constraints to represent multiple consistency levels, and thus provide a way to express preferences, fuzziness, and uncertainty. While there are many soft constraint solving formalisms, even distributed ones, by now there seems to be no concurrent programming framework where soft constraints can be handled. In this paper we show how the classical concurrent constraint (cc) programming framework can work with soft constraints, and we also propose an extension of cc languages which can use soft constraints to prune and direct the search for a solution. We believe that this new programming paradigm, called soft cc (scc), can be also very useful in many web-related scenarios. In fact, the language level allows web agents to express their interaction and negotiation protocols, and also to post their requests in terms of preferences, and the underlying soft constraint solver can find an agreement among the agents even if their requests are incompatible.Comment: 25 pages, 4 figures, submitted to the ACM Transactions on Computational Logic (TOCL), zipped file

An Evolutionary Approach for Learning Attack Specifications in Network Graphs

This paper presents an evolutionary algorithm that learns attack scenarios, called attack specifications, from a network graph. This learning process aims to find attack specifications that minimise cost and maximise the value that an attacker gets from a successful attack. The attack specifications that the algorithm learns are represented using an approach based on Hoare's CSP (Communicating Sequential Processes). This new approach is able to represent several elements found in attacks, for example synchronisation. These attack specifications can be used by network administrators to find vulnerable scenarios, composed from the basic constructs Sequence, Parallel and Choice, that lead to valuable assets in the network

A Constraint-based Approach for Generating Transformation Patterns

Undoing operations is an indispensable feature for many collaborative applications, mainly collaborative editors. It provides the ability to restore a correct state of shared data after erroneous operations. In particular, selective undo allows to undo any operation and is based on rearranging operations in the history thanks to the Operational Transformation (OT) approach. OT is an optimistic replication technique allowing for updating the shared data concurrently while maintaining convergence. It is a challenging task how to meaningfully combine OT and undo approaches. Indeed, undoing operations that are received and executed out-of-order at different sites leads to divergence cases. Even though various undo solutions have been proposed over the recent years, they are either limited or erroneous. In this paper, we propose a constraint-based approach to address the undo problem. We use Constraint Satisfaction Problem (CSP) theory to devise correct and undoable transformation patterns (w.r.t OT and undo properties) which considerably simplifies the design of collaborative objects.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694

Using Distributed CSPs to Model Business Processes Agreement in Software Multiprocess

A business process consists of a set of activities which are performed in a coordination way to obtain an objective. Sometimes the definition of this objective using only a classic business processes management is not possible. When the choreography of the processes cannot be defined with a combination of tasks using sequences, conditions, ’xor’, ’or’ and ’split’ control flow patterns, another representation and solution are necessary to be used. This problem makes difficult the decision making in software management projects. In this paper a way to describe a process agreement is described where the execution and the number of tasks execution order of the Web Services cannot be defined. As a case study, the resource distribution in a multiproject development environment is used. In this case, the processes have to achieve an agreement in function of the business rules that relate the processes. In order to achieve this objective, the Distributed Constraint Satisfaction Problems are used to model and solve this type of problemsJunta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-1371

Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair

High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

A hybrid approach to solving coarse-grained DisCSPs.

A coarse-grained Distributed Constraint Satisfaction Problem (DisCSP) consists of several loosely connected constraint satisfaction subproblems, each assigned to an individual agent. We present Multi-Hyb, a two-phase concurrent hybrid approach for solving DisCSPs. In the first phase, each agents subproblem is solved using systematic search which generates the key partial solutions to the global problem. Concurrently, a penalty-based local search algorithm attempts to find a global solution from these partial solutions. If phase 1 fails to find a solution, a phase 2 systematic search algorithm solves the problem using the knowledge gained from phase 1. We show that our approach is highly competitive in comparison with other coarse-grained DisCSP algorithms