Learning Max-CSPs via Active Constraint Acquisition

Constraint acquisition can assist non-expert users to model their problems as constraint networks. In active constraint acquisition, this is achieved through an interaction between the learner, who posts examples, and the user who classifies them as solutions or not. Although there has been recent progress in active constraint acquisition, the focus has only been on learning satisfaction problems with hard constraints. In this paper, we deal with the problem of learning soft constraints in optimization problems via active constraint acquisition, specifically in the context of the Max-CSP. Towards this, we first introduce a new type of queries in the context of constraint acquisition, namely partial preference queries, and then we present a novel algorithm for learning soft constraints in Max-CSPs, using such queries. We also give some experimental results

Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

This paper shows how we can combine the power of machine learning with the flexibility of constraints. More specifically, we show how machine learning models can be represented by first-order logic theories, and how to derive these theories. The advantage of this representation is that it can be augmented with additional formulae, representing constraints of some kind on the data domain. For instance, new knowledge, or potential attackers, or fairness desiderata. We consider various kinds of learning algorithms (neural networks, k-nearest-neighbours, decision trees, support vector machines) and for each of them we show how to infer the FOL formulae. Then we focus on one particular application domain, namely the field of security and privacy. The idea is to represent the potentialities and goals of the attacker as a set of constraints, then use a constraint solver (more precisely, a solver modulo theories) to verify the satisfiability. If a solution exists, then it means that an attack is possible, otherwise, the system is safe. We show various examples from different areas of security and privacy; specifically, we consider a side-channel attack on a password checker, a malware attack on smart health systems, and a model-inversion attack on a neural network

Optimization with Constraint Learning: A Framework and Survey

Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulas. If data is however available, these data can be used to learn the constraints. The benefits of this approach are clearly seen, however there is a need for this process to be carried out in a structured manner. This paper therefore provides a framework for Optimization with Constraint Learning (OCL) which we believe will help to formalize and direct the process of learning constraints from data. This framework includes the following steps: (i) setup of the conceptual optimization model, (ii) data gathering and preprocessing, (iii) selection and training of predictive models, (iv) resolution of the optimization model, and (v) verification and improvement of the optimization model. We then review the recent OCL literature in light of this framework, and highlight current trends, as well as areas for future research

The complexity and generality of learning answer set programs

Traditionally most of the work in the field of Inductive Logic Programming (ILP) has addressed the problem of learning Prolog programs. On the other hand, Answer Set Programming is increasingly being used as a powerful language for knowledge representation and reasoning, and is also gaining increasing attention in industry. Consequently, the research activity in ILP has widened to the area of Answer Set Programming, witnessing the proposal of several new learning frameworks that have extended ILP to learning answer set programs. In this paper, we investigate the theoretical properties of these existing frameworks for learning programs under the answer set semantics. Specifically, we present a detailed analysis of the computational complexity of each of these frameworks with respect to the two decision problems of deciding whether a hypothesis is a solution of a learning task and deciding whether a learning task has any solutions. We introduce a new notion of generality of a learning framework, which enables us to define a framework to be more general than another in terms of being able to distinguish one ASP hypothesis solution from a set of incorrect ASP programs. Based on this notion, we formally prove a generality relation over the set of existing frameworks for learning programs under answer set semantics. In particular, we show that our recently proposed framework, Context-dependent Learning from Ordered Answer Sets, is more general than brave induction, induction of stable models, and cautious induction, and maintains the same complexity as cautious induction, which has the highest complexity of these frameworks

Automated medical scheduling : fairness and quality

Dans cette thèse, nous étudions les façons de tenir compte de la qualité et de l’équité dans les algorithmes de confection automatique d’horaires de travail. Nous découpons ce problème en deux parties. La modélisation d’un problème d’horaires permet de créer des horaires plus rapidement qu’un humain peut le faire manuellement, puisqu’un ordinateur peut évaluer plusieurs horaires simultanément et donc prendre des décisions en moins de temps. La première partie du problème étudié consiste à améliorer la qualité des horaires en encodant des contraintes et des préférences à l’aide de modèles mathématiques. De plus, puisque la création est plus rapide à l’aide d’un ordinateur, il est plus facile pour un ordinateur de trouver l’horaire ayant la meilleure qualité lorsque les règles et préférences sont clairement définies. Toutefois, déterminer les règles et préférences d’un groupe de personne n’est pas une tâche facile. Ces individus ont souvent de la difficulté à exprimer formellement leurs besoins et leurs préférences. Par conséquent, la création d’un bon modèle mathématique peut prendre beaucoup de temps, et cela même pour un expert en création d’horaires de travail. C’est pourquoi la deuxième partie de cette thèse concerne la réduction du temps de modélisation à l’aide d’algorithmes capable d’apprendre un modèle mathématique à partir de solutions données comme par exemple, dans notre cas, des horaires de travail.In this thesis, we study the ways to take quality and fairness into account in the algorithms of automatic creation of work schedules. We separate this problem into two subproblems. The modeling of a scheduling problem allows a faster creation of schedules than what a human can produce manually. A computer can generate and evaluate multiple schedules at a time and therefore make decisions in less time. This first part of the studied problem consists in improving the quality of medical schedules by encoding constraints and preferences using mathematical models. Moreover, since the creation is faster, it is easier for a computer to find the schedule with the highest quality when the rules and the preferences are clearly defined. However, determining the rules and preferences of a group of people is not an easy task. Those individuals often have difficulties formally expressing their requirements and preferences. Therefore, the creation a good mathematical model might take a long time, even for a scheduling expert. This is why the second part of this thesis concerns the reduction of modeling time using algorithms able to learn mathematical models from given solutions, in our case schedules

Learning constraints and optimization criteria

While there exist several approaches in the constraint programming community to learn a constraint theory, few of them have considered the learning of constraint optimization problems.To alleviate this situation, we introduce an initial approach to learning first-order weighted MAX-SAT theories. It employs inductive logic programming techniques to learn a set of first-order clauses and then uses preference learning techniques to learn the weights of the clauses.In order to learn these weighted clauses, the clausal optimization system uses examples of possible worlds and a set of preferences that state which examples are preferred over other ones.The technique is also empirically evaluated on a number of examples.These experiments show that the system is capable of learning clauses and weights that accurately capture underlying models.status: publishe