Stefano Bistarelli

Semiring-based constraint satisfaction and optimization

We introduce a general framework for constraint satisfaction and optimization where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. The framework is based on a semiring structure, where the set of the semiring specifies the values to be associated with each tuple of values of the variable domain, and the two semiring operations (+ and X) model constraint projection and combination respectively. Local consistency algorithms, as usually used for classical CSPs, can be exploited in this general framework as well, provided that certain conditions on the semiring operations are satisfied. We then show how this framework can be used to model both old and new constraint solving and optimization schemes, thus allowing one to both formally justify many informally taken choices in existing schemes, and to prove that local consistency techniques can be used also in newly defined schemes.

Semiring-Based CSPs and Valued CSPs: Frameworks, Properties,and Comparison

In this paper we describe and compare two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. While comparing the two approaches, we show how to pass from one to the other one, and we discuss when this is possible. The two frameworks have been independently introduced in ijcai95,jacm and schiex-ijcai95.

Semirings for soft constraint solving and programming

1. Introduction.- 2. Soft Constraint Satisfaction Problems.- 3. Towards SCSPs Solutions.- 4. SCSP Abstraction.- 5. Higher Order Semiring-Based Constraints.- 6. Soft CLP.- 7. SCLP and Generalized Shortest Path Problems.- 8. Soft Concurrent Constraint Programming.- 9. Interchangeability in Soft CSPs.- 10. SCSPs for Modelling Attacks to Security Protocols.- 11. Conclusions and Directions for Future Work.

Defense trees for economic evaluation of security investments

In this paper we present a mixed qualitative and quantitative approach for evaluation of information technology (IT) security investments. For this purpose, we model security scenarios by using defense trees, an extension of attack trees with attack countermeasures and we use economic quantitative indexes for computing the defenders return on security investment and the attackers return on attack. We show how our approach can be used to evaluate effectiveness and economic profitability of countermeasures as well as their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.

Semiring-based constraint logic programming: syntax and semantics

We extend the Constraint Logic Programming (CLP) formalism in order to handle semiring-based constraints. This allows us to perform in the same language both constraint solving and optimization. In fact, constraints based on semirings are able to model both classical constraint solving and more sophisticated features like uncertainty, probability, fuzziness, and optimization. We then provide this class of languages with three equivalent semantics: model-theoretic, fix-point, and proof-theoretic, in the style of classical CLP programs.

Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison

In this paper we describe two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. We then compare the two approaches and we discuss the relationship between them. The two frameworks have been independently introduced in [2] and [28].

Strategic games on defense trees

In this paper we use defense trees, an extension of attack trees with countermeasures, to represent attack scenarios and game theory to detect the most promising actions attacker and defender. On one side the attacker wants to break the system (with as little efforts as possible), on the opposite side the defender want to protect it (sustaining the minimum cost). As utility function for the attacker and for the defender we consider economic indexes (like the Return on Investment (ROI) and the Return on Attack (ROA)). We show how our approach can be used to evaluate effectiveness and economic profitability of countermeasures as well as their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.

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, as yet there seems to be no concurrent programming framework where soft constraints can be handled. In this article 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.

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 algorithms, 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 very useful in many webrelated 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.

ConArg: A Constraint-Based Computational Framework for Argumentation Systems

We propose ConArg, a tool based on Constraint Programming, to model and solve various problems related to the Argumentation research field. Constraint Satisfaction Problems (CSPs) offer a wide number of efficient techniques (as inference and search algorithms) that can tackle the complexity in finding all the possible Dungs conflict-free, admissible, complete, stable, preferred and grounded extensions in Argumentation Frameworks. Moreover, we can use the tool to solve some computationally hard problems presented in [1]. To implement ConArg, we have used JaCoP, a Java library which provides the user with a Finite Domain Constraint Programming paradigm, to model and solve these two problems. ConArg is able to randomly generate two different kinds of small-world networks in order to find Dungs extensions on such interaction graphs. We present the main features of ConArg and the reported performance in time.