Bessiere et al. (AAAI'08) showed that several intractable global constraints can be efficiently propagated when certain natural problem parameters are small. In particular, the complete propagation of a global constraint is fixed-parameter tractable in k - the number of holes in domains - whenever bound consistency can be enforced in polynomial time; this applies to the global constraints AtMost-NValue and Extended Global Cardinality (EGC). In this paper we extend this line of research and introduce the concept of reduction to a problem kernel, a key concept of parameterized complexity, to the field of global constraints. In particular, we show that the consistency problem for AtMost-NValue constraints admits a linear time reduction to an equivalent instance on O(k^2) variables and domain values. This small kernel can be used to speed up the complete propagation of NValue constraints. We contrast this result by showing that the consistency problem for EGC constraints does not admit a reduction to a polynomial problem kernel unless the polynomial hierarchy collapses.

Heuristics are crucial tools in decreasing search effort in varied fields of AI. In order to be effective, a heuristic must be efficient to compute, as well as provide useful information to the search algorithm. However, some well-known heuristics which do well in reducing backtracking are so heavy that the gain of deploying them in a search algorithm might be outweighed by their overhead. We propose a rational metareasoning approach to decide when to deploy heuristics, using CSP backtracking search as a case study. In particular, a value of information approach is taken to adaptive deployment of solution-count estimation heuristics for value ordering. Empirical results show that indeed the proposed mechanism successfully balances the tradeoff between decreasing backtracking and heuristic computational overhead, resulting in a significant overall search time reduction.

Preference-based reasoning is a form of commonsense reasoning that makes many problems easier to express and sometimes more likely to have a solution. We present an approach to introduce preferences in the weight constraint construct, which is a very useful programming construct widely adopted in Answer Set Programming (ASP). We show the usefulness of the proposed extension, and we outline how to accordingly extend the ASP semantics.

We consider the problem of assessing the sustainability of alternative designs (e.g., for an urban environment) that are assembled from multiple components (e.g., water supply, transportation system, shopping centers, commercial spaces, parks). We model the sustainability of a design in terms of a set of sustainability attributes. Given the (qualitative) preferences and tradeoffs of decision makers over the sustainability attributes, we formulate the problem of identifying sustainable designs as the problem of finding the most preferred designs with respect to those preferences. We show how techniques for representing and reasoning with qualitative preferences can be used to identify the most preferred designs based on the decision maker’s stated preferences and tradeoffs.

Figure 1a first illustrates by the oval that a Cradle-to-cradle (C2C) design (McDonough & Braungart, critical problem in traditional design is that a product is designed 2002) recognizes that nothing short of full recycling of materials in isolation. In contrast, the products shown in the with no degradation in material quality is necessary square box of Figure 1b illustrate the concept of a product for long-term planet sustainability. C2C advocates looking family, where multiple products are designed within a system to the natural world as an ideal model of recycling, where of material use and reuse, which flows between product organic materials are continually recycled through processes lines. While there may still be materials that come from of decay and growth. They propose design methodology outside the family and there are materials that are byproducts that separates biological cycles and syntheticmaterial of the family production, a family design would seek cycles, enabling biological material to be reclaimed to minimize these and to exploit them in a still larger context.

We propose AllDiffPrecedence, a new global constraint that combines together an AllDifferent constraint with precedence constraints that strictly order given pairs of variables. We identify a number of applications for this global constraint including instruction scheduling and symmetry breaking. We give an efficient propagation algorithm that enforces bounds consistency on this global constraint. We show how to implement this propagator using a decomposition that extends the bounds consistency enforcing decomposition proposed for the AllDifferent constraint. Finally, we prove that enforcing domain consistency on this global constraint is NP-hard in general.

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i) its probability of type I error is below a pre-specified level and (ii), it has probability of type II error close to the minimum possible. The proposed classifier is obtained by solving an optimization problem with an empirical objective and an empirical constraint. New techniques to handle such problems are developed and have consequences on chance constrained programming.

The study of phase transition phenomenon of NP complete problems plays an important role in understanding the nature of hard problems. In this paper, we follow this line of research by considering the problem of counting solutions of Constraint Satisfaction Problems (#CSP). We consider the random model, i.e. RB model. We prove that phase transition of #CSP does exist as the number of variables approaches infinity and the critical values where phase transitions occur are precisely located. Preliminary experimental results also show that the critical point coincides with the theoretical derivation. Moreover, we propose an approximate algorithm to estimate the expectation value of the solutions number of a given CSP instance of RB model.

Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any linear transformation of either or both of the views; for tasks with small sample size such flexibility implies overfitting, which is even worse for more flexible nonparametric or kernel-based dependency discovery methods. We develop variants which reduce the degrees of freedom by assuming constraints on similarity of the projections in the two views. A particular example is provided by a cancer gene discovery application where chromosomal distance affects the dependencies between gene copy number and activity levels. Similarity constraints are shown to improve detection performance of known cancer genes.

We at the NASA laboratory believed that our best work came when we simultaneously advanced AI theory and provided immediately usable solutions for current NASA problems. "Solving Large-Scale Constraint Satisfaction and Scheduling Problems Using a Heuristic Repair Method," by Steve Minton, Mark Johnston, Andy Phillips, and Phil Laird clearly achieved both. It proved that local search and repair was applicable to a wide class of constraint satisfaction problems and clearly explicated the theory behind that proof.