The key assumption in Weighted Constraint Satisfaction Problems (WCSPs) is that all constraints are specified a priori. This assumption does not hold in some applications that involve users preferences. Incomplete WCSPs (IWCSPs) extend WCSPs by allowing some constraints to be partially specified. Unfortunately, existing IWCSP approaches either guarantee to return optimal solutions or not provide any quality guarantees on solutions found. To bridge the two extremes, we propose a number of parameterized heuristics that allow users to find boundedly-suboptimal solutions, where the error bound depends on user-defined parameters. These heuristics thus allow users to trade off solution quality for fewer elicited preferences and faster computation times.

The key assumption in Weighted Constraint Satisfaction Problems (WCSPs) is that all constraints are specified a priori. This assumption does not hold in some applications that involve users preferences. Incomplete WCSPs (IWCSPs) extend WCSPs by allowing some constraints to be partially specified. Unfortunately, existing IWCSP approaches either guarantee to return optimal solutions or not provide any quality guarantees on solutions found. To bridge the two extremes, we propose a number of parameterized heuristics that allow users to find boundedly-suboptimal solutions, where the error bound depends on user-defined parameters. These heuristics thus allow users to trade off solution quality for fewer elicited preferences and faster computation times.

Researchers have used Distributed Constraint Optimization Problems (DCOPs) as a powerful approach to model various multi-agent coordination problems, taking into account their preferences and constraints. A core limitation of this model is the assumption that all agents’ preferences are specified a priori. However, in a number of application domains such knowledge become available only after being elicited from users in these domains. In this abstract, we explore the effects of preference elicitation in our motivating application of scheduling smart appliances with the aim of reducing users’ electricity bill cost as well as increasing their comfort.

Distributed Constraint Optimization Problems (DCOPs) are commonly used to model multi-agent coordination problems. However, empirical evaluations of DCOP algorithms are typically done in simulation under the assumption that the communication times between all pairs of agents are identical, which is unrealistic in many real-world applications. In this paper, we investigate the impact of empirically evaluating a DCOP algorithm under the assumption that communication times between pairs of agents can vary and propose the use of ns-2, a de-facto simulator used by the computer networking community, to simulate the communication times. Additionally, we introduce heuristics that exploit the non- uniform communication times to speed up DCOP algorithms that operate on pseudo-trees.

Empirical evaluations of DCOP algorithms are typically done in simulation and under the assumption that the communication times between all pairs of agents are identical, which is unrealistic in many real-world applications. In this abstract, we incorporate non-uniform communication times in the default DCOP model and propose heuristics that exploit these communication times to speed up DCOP algorithms that operate on pseudo-trees.