El Houssine Bouyakhf

Adaptive Parameterized Consistency

State-of-the-art constraint solvers uniformly maintain the same level of local consistency usually arc consistency on all the instances. We propose parameterized local consistency, an original approach to adjust the level of consistency depending on the instance and on which part of the instance we propagate. We do not use as parameter one of the features of the instance, as done for instance in portfolios of solvers. We use as parameter the stability of values, which is a feature based on the state of the arc consistency algorithm during its execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency on a value depending on whether the stability of the value is above or below a given threshold. We also propose a way to dynamically adapt the parameter, and thus the level of local consistency, during search. This approach allows us to get a good trade-off between the number of values pruned and the computational cost. We validate our approach on various problems from the CSP competition.

Nogood-based asynchronous forward checking algorithms

We propose two new algorithms for solving Distributed Constraint Satisfaction Problems (DisCSPs). The first algorithm, AFC-ng, is a nogood-based version of Asynchronous Forward Checking (AFC). Besides its use of nogoods as justification of value removals, AFC-ng allows simultaneous backtracks going from different agents to different destinations. The second algorithm, Asynchronous Forward Checking Tree (AFC-tree), is based on the AFC-ng algorithm and is performed on a pseudo-tree ordering of the constraint graph. AFC-tree runs simultaneous search processes in disjoint problem subtrees and exploits the parallelism inherent in the problem. We prove that AFC-ng and AFC-tree only need polynomial space. We compare the performance of these algorithms with other DisCSP algorithms on random DisCSPs and instances from real benchmarks: sensor networks and distributed meeting scheduling. Our experiments show that AFC-ng improves on AFC and that AFC-tree outperforms all compared algorithms, particularly on sparse problems.

Asynchronous inter-level forward-checking for DisCSPs

We propose two new asynchronous algorithms for solving Distributed Constraint Satisfaction Problems (DisCSPs). The first algorithm, AFC-ng, is a nogood-based version of Asynchronous Forward Checking (AFC). The second algorithm, Asynchronous Inter-Level Forward-Checking (AILFC), is based on the AFC-ng algorithm and is performed on a pseudo-tree ordering of the constraint graph. AFC-ng and AILFC only need polynomial space. We compare the performance of these algorithms with other DisCSP algorithms on random DisCSPs in two kinds of communication environments: Fast communication and slow communication. Our experiments show that AFC-ng improves on AFC and that AILFC outperforms all compared algorithms in communication load.

Boosting constraint acquisition via generalization queries

Constraint acquisition assists a non-expert user in modeling her problem as a constraint network. In existing constraint acquisition systems the user is only asked to answer very basic questions. The drawback is that when no background knowledge is provided, the user may need to answer a great number of such questions to learn all the constraints. In this paper, we introduce the concept of generalization query based on an aggregation of variables into types. We present a constraint generalization algorithm that can be plugged into any constraint acquisition system. We propose several strategies to make our approach more efficient in terms of number of queries. Finally we experimentally compare the recent QUACQ system to an extended version boosted by the use of our generalization functionality. The results show that the extended version dramatically improves the basic QUACQ.

Agile Asynchronous Backtracking for Distributed Constraint Satisfaction Problems

Asynchronous Backtracking is the standard search procedure for distributed constraint reasoning. It requires a total ordering on the agents. All polynomial space algorithms proposed so far to improve Asynchronous Backtracking by reordering agents during search only allow a limited amount of reordering. In this paper, we propose Agile-ABT, a search procedure that is able to change the ordering of agents more than previous approaches. This is done via the original notion of termination value, a vector of stamps labelling the new orders exchanged by agents during search. In Agile-ABT, agents can reorder themselves as much as they want as long as the termination value decreases as the search progresses. Our experiments show the good performance of Agile-ABT when compared to other dynamic reordering techniques.

Compilation Formulation for Asynchronous Backtracking with Complex Local Problems

The Asynchronous BackTracklng (ABT) algorithm is a well known algorithm for solving distributed constraint satisfaction problems. However, several work which interest to ABT suppose that each agent owns one single variable. In this paper, we present the compilation formulation for Asynchronous Backtracking with complex local problems, resulting in the ABT-cf algorithm. The ABT-cf algorithm is described in detail and its correctness proof. An extensive experimental evaluation of the proposed algorithm is carried on random binary DisCSP. The performances of ABT-cf is compared to the standard ABT in which the distributed problem is reformulated by decomposition. Experimental evaluation shows that ABT-cf increases the performance of the distributed search and outperforms standard ABT by a large scale.

MP-ABT: A Minimal Perturbation Approach for Complex Local Problems.

The ability of Distributed Constraints Reasoning (DCR) to solve distributed combinatorial problems brings the DCR to have a considerable interest in multi-agent community. Hence, many DisCSP algorithms have been proposed in order to solve such distributed problems. The major limit of these algorithms is the simplification assumptions. The scientists assume that each agent is a simple one; it handles just one variable. But in the complex local problem case; where each agent has more than one variable; two methods are used: The compilation and the decomposition. These methods transform the original problem so as to make it as a simple one. In this paper, we propose a new protocol: MP-ABT (Minimal Perturbation complex local problems in the Asynchronous Backtracking). It is a resolution algorithm of DisCSPs with complex local problems. It is based on the ABT algorithm and the Dynamic CSP. Each complex agent is seen as a Minimal Perturbation Problem (MPP) and any received message is considered as a new intra-constraint perturbation event. The complex local problem is updated and a new MPP local solution is reported. The MP-ABT is presented and compared to three ABT families. Our experimental results show the MP-ABT effectiveness.

Dynamic JChoc: A Distributed Constraints Reasoning Platform for Dynamically Changing Environments

In Artificial Intelligence, a large number of problems (i.e. distributed resource management, distributed air traffic management, Distributed Sensor Network [1]) can be modeled and solved as Distributed Constraint Satisfaction Problems (DisCSPs). As many real world problems change continuously and incessantly over time, some methods have been developed (e.g. DynABT), for solving problems which exhibit this dynamic behavior. Meanwhile, there was no available framework that helped users to develope intelligent multi-agent systems based on Dynamic and Distributed Constraints Reasoning (DCR) techniques.

Maintaining Arc Consistency Asynchronously in Synchronous Distributed Search

We recently proposed No good-Based Asynchronous Forward Checking (AFC-ng), an efficient and robust algorithm for solving Distributed Constraint Satisfaction Problems (DisCSPs). AFC-ng performs an asynchronous forward checking phase during synchronous search. In this paper, we propose two new algorithms based on the same mechanism as AFC-ng. However, instead of using forward checking as a filtering property, we propose to maintain arc consistency asynchronously (MACA). The first algorithm we propose, MACA-del, enforces arc consistency thanks to an additional type of messages, deletion messages. The second algorithm, MACA-not, achieves arc consistency without any new type of message. We provide a theoretical analysis and an experimental evaluation of the proposed approach. Our experiments show the good performance of MACA algorithms, particularly those of MACA-not.

Extended Partial-Order Dynamic Backtracking Algorithm for Dynamically Changed Environments

Unlike constructive approaches in which a partial assignment to the variables is incrementally extended, repair approaches start with an inconsistent assignment (e.g. old solution) and search through the space of possible repairs. In this paper, we propose an Extended Partial-order Dynamic Backtracking (EPDB) algorithm for dynamically changed environments. The EPDB allows dynamic CSPs to be dealt efficiently according to based-repair heuristic approaches. A past solution can be repaired using retroactive data structures: safety conditions and no goods, saved previously in Partial-order Dynamic Backtracking process. We evaluate our algorithm on synthetic and real problems, and experimental results show that the proposed algorithm outperforms the original algorithm PDB, in terms of run-time and constraints checks.