Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. In addition, we propose a new pattern decomposition algorithm, called AMIQO, based on solving a sequence of (mixed-integer) quadratic programs. Our approach considerably outperforms the state of the art on the materials discovery problem, scaling to larger datasets and recovering more precise and physically meaningful decompositions. We also show the effectiveness of our approach for enforcing background knowledge on other application domains.

Dynamic programming (DP) is a fundamental tool used to obtain exact, optimal solutions for many combinatorial optimization problems. Among these problems, important ones including the knapsack problems and the computation of edit distances between string pairs can be solved with a kind of DP that corresponds to solving the shortest path problem on a directed acyclic graph (DAG). These problems can be solved efficiently with DP, however, in practical situations, we want to solve the customized problems made by adding logical constraints to the original problems. Developing an algorithm specifically for each combination of a problem and a constraint set is unrealistic. The proposed method, BDD-Constrained Search (BCS), exploits a Binary Decision Diagram (BDD) that represents the logical constraints in combination with the DAG that represents the problem. The BCS runs DP on the DAG while using the BDD to check the equivalence and the validity of intermediate solutions to efficiently solve the problem. The important feature of BCS is that it can be applied to problems with various types of logical constraints in a unified way once we represent the constraints as a BDD. We give a theoretical analysis on the time complexity of BCS and also conduct experiments to compare its performance to that of a state-of-the-art integer linear programming solver.

We develop a new approach for a pre-disaster planning problem which consists in computing an optimal investment plan to strengthen a transportation network, given that a future disaster probabilistically destroys links in the network. We show how the problem can be formulated as a non-linear integer program and devise an AI algorithm to solve it. In particular, we introduce a new type of extreme resource constraint and develop a practically efficient propagation algorithm for it. Experiments show several orders of magnitude improvements over existing approaches, allowing us to close an existing real-world benchmark and to solve to optimality other, more challenging benchmarks.

Computing prices in core-selecting combinatorial auctions is a computationally hard problem. Auctions with many bids can only be solved using a recently proposed core constraint generation (CCG) algorithm, which may still take days on hard instances. In this paper, we present a new algorithm that significantly outperforms the current state of the art. Towards this end, we first provide an alternative definition of the set of core constraints, where each constraint is weakly stronger, and prove that together these constraints define the identical polytope to the previous definition. Using these new theoretical insights we develop two new algorithmic techniques which generate additional constraints in each iteration of the CCG algorithm by 1) exploiting separability in allocative conflicts between participants in the auction, and 2) by leveraging non-optimal solutions. We show experimentally that our new algorithm leads to significant speed-ups on a variety of large combinatorial auction problems. Our work provides new insights into the structure of core constraints and advances the state of the art in fast algorithms for computing core prices in large combinatorial auctions.

This paper proposes incremental preference elicitation methods for multiobjective state space search. Our approach consists in integrating weight elicitation and search to determine, in a vector-valued state-space graph, a solution path that best fits the Decision Maker's preferences. We first assume that the objective weights are imprecisely known and propose a state space search procedure to determine the set of possibly optimal solutions. Then, we introduce incremental elicitation strategies during the search that use queries to progressively reduce the set of admissible weights until a nearly-optimal path can be identified. The validity of our algorithms is established and numerical tests are provided to test their efficiency both in terms of number of queries and solution times.

This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i)~It shows how one can formulate DCOPs as logic programs; (ii)~It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (iii)~It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative-programming counterpart) as well as solve some problems that DPOP fails to solve due to memory limitations; and (iv)~It demonstrates the applicability of ASP in the wide array of multi-agent problems currently modeled as DCOPs.

To achieve high performance, autonomous systems, such as science explorers, should adapt to the environment to improve utility gained, as well as robustness. Flexibility during temporal plan execution has been explored extensively to improve robustness, where flexibility exists both in activity choices and schedules. These problems are framed as conditional constraint networks over temporal constraints. However, flexibility has been exploited in a limited form to improve utility. Prior work considers utility in choice or schedule, but not their coupling. To exploit fully flexibility, we introduce conditional simple temporal networks with preference (CSTNP), where preference is a function over both choice and schedule. Enumerating best solutions to a CSTNP is challenging due to the cost of scheduling a candidate STPP and the exponential number of candidates. Our contribution is an algorithm for enumerating solutions to CSTNPs efficiently, called A star with bounding conflicts (A*BC), and a novel variant of conflicts, called bounding conflicts, for learning heuristic functions. A*BC interleaves Generate, Test, and Bound. When A*BC bounds a candidate, by solving a STPP, it generates a bounding conflict, denoting neighboring candidates with similar bounds. A*BC's generator then uses these conflicts to steer away from sub-optimal candidates.

Among the languages used for representing goals, actions and their consequences on the world for decision making and planning, GDL (Game Description Language) has the ability to represent complex actions in potentially uncertain and competitive environments. The aim of this paper is to exploit stochastic constraint networks in order to provide compact representations of strategic games, and to identify optimal policies in those games with generic forward checking method. From this perspective, we develop a compiler allowing to translate games, described in GDL, into instances of the Stochastic Constraint Optimization Problem (SCSP). Our compiler is proved correct for the class GDL of games with complete information and oblivious environment. The interest of our approach is illustrated by solving several GDL games with a SCSP solver.

This paper outlines a new approach to creating value from the Smart Grid by incorporating individual households into the response system that must be deployed to accommodate increasingly large sources of intermittent renewable power. We propose a framework that couples agent-based AI techniques with envelope methods. Envelope methods provide a unified mathematical framework to model intermittent renewable resources, conventional dispatchable resources, demand side response, and storage. The overall goal of our system is to develop a distributed autonomous agent architecture that is able to facilitate market transactions among load serving entities, residential consumers, conventional merchant power producers, and intermittent power producers.

In this challenge paper, we consider the importance of preferences in smart homes and assistive environments and discuss the potential application of models and algorithms developed within the computational preferences community. We suggest the value of future research collaborations.