Kidney exchanges are organized markets where patients swap willing but incompatible donors. In the last decade, kidney exchanges grew from small and regional to large and national---and soon, international.  This growth results in more lives saved, but exacerbates the empirical hardness of the NP-complete problem of optimally matching patients to donors.  State-of-the-art matching engines use integer programming techniques to clear fielded kidney exchanges, but these methods must be tailored to specific models and objective functions, and may fail to scale to larger exchanges. In this paper, we observe that if the kidney exchange compatibility graph can be encoded by a constant number of patient and donor attributes, the clearing problem is solvable in polynomial time. We give necessary and sufficient conditions for losslessly shrinking the representation of an arbitrary compatibility graph. Then, using real compatibility graphs from the UNOS US-wide kidney exchange, we show how many attributes are needed to encode real graphs. The experiments show that, indeed, small numbers of attributes suffice.

Recently a large attention has been devoted to the ethical issues arising around the design and the implementation of artificial agents. This is due to the fact that humans and machines more and more often need to collaborate to decide on actions to take or decisions to make. Such decisions should be not only correct and optimal from the point of view of the overall goal to be reached, but should also agree to some form of moral values which are aligned to the human ones. Examples of such scenarios can be seen in autonomous vehicles, medical diagnosis support systems, and many other domains, where humans and artificial intelligent systems cooperate. One of the main issues arising in this context regards ways to model and reason with moral values. In this paper we discuss the possible use of AI compact preference models as a promising approach to model, reason, and embed moral values in decision support systems.

This paper deals with the constrained DAG shortest path problem (CDSP), which finds the shortest path on a given directed acyclic graph (DAG) under any logical constraints posed on taken edges. There exists a previous work that uses binary decision diagrams (BDDs) to represent the logical constraints, and traverses the input DAG and the BDD simultaneously. The time complexity of this BDD-based method is derived from BDD size, and tends to be fast only when BDDs are small. However, since it does not prioritize the search order, there is considerable room for improvement, particularly for large BDDs. We combine the well-known A* search with the BDD-based method synergistically, and implement several novel heuristic functions. The key insight here is that the ‘shortest path’ in the BDD is a solution of a relaxed problem, just as the shortest path in the DAG is. Experiments, particularly practical machine learning applications, show that the proposed method deceases search time by up to 2 orders of magnitude, with the specific result that it is 2,000 times faster than a commercial solver.

Symmetry-breaking is a technique for efficiently solving SAT instances that contain high degrees of symmetry among the variables of the instance. When satisfiability problems are represented as a relational schema, symmetries between objects in the domain can be detected directly from evidence, that is, variables known to have a particular setting prior to solving. These symmetries between domain objects are called term symmetries. In this work, we present two novel extensions to the technique of term equivalent symmetry breaking which allow the detection and exploitation of conditional or hidden symmetries, those relationships between domain objects that are obscured until the instance is partially solved. We give promising preliminary experimental results for this technique, and discuss how the techniques could be extended for use in probabilistic domains.

Given an agricultural watershed containing a set of spatial units, and a set of land management practices, the Geodesign Optimization (GOP) aims to find a land management practice for each spatial unit that optimizes overall water quality improvements in the watershed under both budget constraint and spatial constraints (e.g., minimum contiguous area, shape) arising from farm equipment operation practicalities. GOP is important for redesign of agricultural watersheds in Midwestern US to mitigate soil and water quality degradation and loss of habitat. The problem is computationally challenging as a large-scale combinatorial problem (NP-hard) under spatial constraints. Existing optimization techniques do not address spatial constraints, and lead to impractical solutions requiring frequent farm equipment reconfiguration. In this paper, we formalize the spatially-constrained GOP and propose a novel spatial optimizer which explores optimal solution without constraint violations. Our approach is further validated through a Geodesign case study at Seven Mile Creek watershed in Midwestern US.

The paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques.

This thesis deals with gradience in grammar, i.e., with the fact that some linguistic structures are not fully acceptable or unacceptable, but receive gradient linguistic judgments. The importance of gradient data for linguistic theory has been recognized at least since Chomsky's Logical Structure of Linguistic Theory. However, systematic empirical studies of gradience are largely absent, and none of the major theoretical frameworks is designed to account for gradient data.

Free Online AI course, Berkeley's CS 188, offered through edX. Free Online AI course, Berkeley's CS 188, offered through edX.

We propose a new family of message passing techniques for MAP estimation in graphical models which we call {\em Sequential Reweighted Message Passing} (SRMP). Special cases include well-known techniques such as {\em Min-Sum Diffusion} (MSD) and a faster {\em Sequential Tree-Reweighted Message Passing} (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. We present such a generalization for the case of higher-order graphical models, and test it on several real-world problems with promising results.

The Traveling Salesman Problem is one of the most intensively studied problems in computational mathematics. These pages are devoted to the history, applications, and current research of this challenge of finding the shortest route visiting each member of a collection of locations and returning to your starting point.