A methodology for the elicitation of redesign knowledge.

We present MADAM, a methodology which allows the elicitation, capture, analysis and management of redesign knowledge. This area is characterised by the high reusability of problem solutions and is represented using three views: physical, functional and process. The methodology supports the analysis of the knowledge elicited and, therefore, the inconsistencies are detected. In addition, the knowledge is normalised so unnecessary (subsumed) parts and technical solutions can be removed with the aid of the expert. MADAM thus contributes towards better and faster redesign

Escaping local optima: constraint weights vs value penalties.

Constraint Satisfaction Problems can be solved using either iterative improvement or constructive search approaches. Iterative improvement techniques converge quicker than the constructive search techniques on large problems, but they have a propensity to converge to local optima. Therefore, a key research topic on iterative improvement search is the development of effective techniques for escaping local optima, most of which are based on increasing the weights attached to violated constraints. An alternative approach is to attach penalties to the individual variable values participating in a constraint violation. We compare both approaches and show that the penalty-based technique has a more dramatic effect on the cost landscape, leading to a higher ability to escape local optima. We present an improved version of an existing penalty-based algorithm where penalty resets are driven by the amount of distortion to the cost landscape caused by penalties. We compare this algorithm with an algorithm based on constraint weights and justify the difference in their performance

Inference fusion: a hybrid approach to taxonomic reasoning.

We present a hybrid way to extend taxonomic reasoning using inference fusion, i.e. the dynamic combination of inferences from distributed heterogeneous reasoners. Our approach integrates results from a DL-based taxonomic reasoner with results from a constraint solver. Inference fusion is carried out by (i) parsing heterogeneous input knowledge, producing suitable homogeneous subset of the input knowledge for each specialised reasoner; (ii) processing the homogeneous knowledge, collecting the reasoning results and passing them to the other reasoner if appropriate; (iii) combining the results of the two reasoners. We discuss the benefits of our approach to the ontological reasoning and demonstrate our ideas by proposing a hybrid modelling languages, DL(D)=S, and illustrating its use by means of examples

CSP: there is more than one way to model it.

In this paper, we present an approach for conceptual modelling of con- straint satisfaction problems (CSP). The main objective is to achieve a similarly high degree of modelling support for constraint problems as it is already available in other disciplines. The approach uses diagrams as operational basis for the development of CSP models. To facilitate a broader scope, the use of available mainstream modelling languages is adapted. In particular, the structural aspects of the problem are visually expressed in UML, complemented by a textual representation of rela- tions and constraints in OCL. A case study illustrates the expositions and deployment of the approach

DynABT: dynamic asynchronous backtracking for dynamic DisCSPs.

Constraint Satisfaction has been widely used to model static combinatorial problems. However, many AI problems are dynamic and take place in a distributed environment, i.e. the problems are distributed over a number of agents and change over time. Dynamic Distributed Constraint Satisfaction Problems (DDisCSP) [1] are an emerging field for the resolution problems that are dynamic and distributed in nature. In this paper, we propose DynABT, a new Asynchronous algorithm for DDisCSPs which combines solution and reasoning reuse i.e. it handles problem changes by modifying the existing solution while re-using knowledge gained from solving the original (unchanged) problem. The benefits obtained from this approach are two-fold: (i) new solutions are obtained at a lesser cost and; (ii) resulting solutions are stable i.e. close to previous solutions. DynABT has been empirically evaluated on problems of varying difficulty and several degrees of changes has been found to be competitive for the problem classes tested

DisBO-wd: a distributed constraint satisfaction algorithm for coarse-grained distributed problems.

We present a distributed iterative improvement algorithm for solving coarse-grained distributed constraint satisfaction problems (DisCSPs). Our algorithm is inspired by the Distributed Breakout for coarse-grained DisCSPs where we introduce a constraint weight decay and a constraint weight learning mechanism in order to escape local optima. We also introduce some randomisation in order to give the search a better chance of finding the right path to a solution. We show that these mechanisms improve the performance of the algorithm considerably and make it competitive with respect to other algorithms

Dynamic agent prioritisation with penalties in distributed local search.

Distributed Constraint Satisfaction Problems (DisCSPs) solving techniques solve problems which are distributed over a number of agents.The distribution of the problem is required due to privacy, security or cost issues and, therefore centralised problem solving is inappropriate. Distributed local search is a framework that solves large combinatorial and optimization problems. For large problems it is often faster than distributed systematic search methods. However, local search techniques are unable to detect unsolvability and have the propensity of getting stuck at local optima. Several strategies such as weights on constraints, penalties on values and probability have been used to escape local optima. In this paper, we present an approach for escaping local optima called Dynamic Agent Prioritisation and Penalties (DynAPP) which combines penalties on variable values and dynamic variable prioritisation for the resolution of distributed constraint satisfaction problems. Empirical evaluation with instances of random, meeting scheduling and graph colouring problems have shown that this approach solved more problems in less time at the phase transition when compared with some state of the art algorithms. Further evaluation of the DynAPP approach on iteration-bounded optimisation problems showed that DynAPP is competitive

Multi-HDCS: solving DisCSPs with complex local problems cooperatively.

We propose Multi-HDCS, a new hybrid approach for solving Distributed CSPs with complex local problems. In Multi-HDCS, each agent concurrently: (i) runs a centralised systematic search for its complex local problem; (ii) participates in a distributed local search; (iii) contributes to a distributed systematic search. A centralised systematic search algorithm runs on each agent, finding all non-interchangeable solutions to the agents complex local problem. In order to find a solution to the overall problem, two distributed algorithms which only consider the local solutions found by the centralised systematic searches are run: a local search algorithm identifies the parts of the problem which are most difficult to satisfy, and this information is used in order to find good dynamic variable orderings for a systematic search. We present two implementations of our approach which differ in the strategy used for local search: breakout and penalties on values. Results from an extensive empirical evaluation indicate that these two Multi-HDCS implementations are competitive against existing distributed local and systematic search techniques on both solvable and unsolvable distributed CSPs with complex local problems