, the tradeoffs in complexity involved in moving toward mediation based approaches are not. In this paper we attempt to comprehensively reexamine the space of mediation based approaches for DCSP and fill gaps in existing frameworks with new strategies. We present different mediation session selection rules

Abstract not found

approaches are not. In this paper we comprehensively reexamine the space of mediation based approaches for DCSP and fill gaps in existing frameworks with new strategies. We present different mediation session selection rules, including a rule that favors smaller mediation sessions, and different mediation

) provides categorization of problem settings with big speedups by the DCSP strategies based on extra communication, enabling us to selectively apply the strategies to a given domain. This paper not only provides a useful method for performance measurement to the DCSP community, but also shows the utility

This paper describes an algorithm designed to minimally recongure schedules in response to a changing environment. External factors have caused an existing schedule to become invalid, perhaps due to the withdrawal of resources, or because of changes to the set of scheduled activities. The total shift in the start and end times of already scheduled activities should be kept to a minimum. This optimization requirement may be captured using a linear optimization function over linear constraints. However, the disjunctive nature of the resource constraints impairs traditional mathematical programming approaches. The unimodular probing algorithm interleaves constraint programming and linear programming. The linear programming solver handles only a controlled subset of the problem constraints, to guarantee that the values returned are discrete. Using probe backtracking, a complete, repair-based method for search, these values are simply integrated into constraint programming. Unimodular p...

Many AI problems can be modeled as constraint satisfaction problems (CSP), but many of them are actually dynamic: the set of constraints to consider evolves because of the environment, the user or the other agents in the framework of a distributed system. The notion of dynamic CSP (DCSP) has been

There has been an increasing recognition that a number of key computational problems require distributed solution techniques. To facilitate the creation and advancement of these techniques, researchers have developed the distributed constraint satisfaction (DCSP) formalism with the under

The Dynamic Constraint Satisfaction Problem (DCSP) formalism has been gaining attention as a valuable and often necessary extension of the static CSP framework. Dynamic Constraint Satisfaction enables CSP techniques to be applied more extensively, since it can be applied in domains where

When search techniques are used to solve a practical problem, the solution produced is often brittle in the sense that small execution difficulties can have an arbitrarily large effect on the viability of the solution. The AI community has responded to this difficulty by investigating

This paper develops a fuzzy constraint based model for bilateral multi-issue negotiation in trading environments. In particular, we are concerned with the principled negotiation approach in which agents seek to strike a fair deal for both parties, but which, nevertheless, maximises their own payoff. Thus, there are elements of both competition and cooperation in the negotiation (hence semi-competitive environments) . One of the key intuitions of the approach is that there is often more than one option that can satisfy the interests of both parties. So, if the opponent cannot accept an offer then the proponent should endeavour to find an alternative that is equally acceptable to it, but more acceptable to the opponent. That is, the agent should make a trade-off. Only if such a trade-off is not possible should the agent make a concession. Against this background, our model ensures the agents reach a deal that is fair (Pareto-optimal) for both parties if such a solution exists. Moreover, this is achieved by minimising the amount of private information that is revealed. The model uses prioritised fuzzy constraints to represent trade-offs between the different possible values of the negotiation issues and to indicate how concessions should be made when they are necessary. Also by using constraints to express negotiation proposals, the model can cover the negotiation space more ef- ficiently since each exchange covers a region rather than a single point (which is what most existing models deal with). In addition, by incorporating the notion of a reward into our negotiation model, the agents can sometimes reach agreements that would not otherwise be possible.