When an optimal solution is not required, satisficing search methods such as greedy best-first search are often used to find solutions quickly. In work on satisficing search, there has been substantial attention devoted to how to solve problems associated with local minima or plateaus in the heuristic function. One technique that has been shown to be quite promising is using an alternative heuristic function that does not estimate cost-to-go, but rather estimates distance-to-go. There is currently little beyond intuition to explain its superiority. We begin by empirically showing that the success of the distance-to-go heuristic appears related to its having smaller local minima. We then discuss a reasonable theoretical model of heuristics and show that, under this model, the expected size of local minima is higher for a cost-to-go heuristic than a distance-to-go heuristic, offering a possible explanation as to why distance-to-go heuristics tend to outperform cost-to-go heuristics.

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding a longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and close relationships between Dijkstra's algorithm, weighted A*, and depth-first search.

Location-based services allow users to perform geo-spatial recording actions, which facilitates the mining of the moving activities of human beings. This paper proposes a system, TimeRouter, to recommend time-sensitive trip routes consisting of a sequence of locations with associated time stamps based on knowledge extracted from large-scale location check-in data. We first propose a statistical route goodness measure considering: (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. Then we construct the time-sensitive route recommender with two major functions: (1) constructing the route based on the user-specified source location with the starting time, (2) composing the route between the specified source location and the destination location given a starting time. We devise a search method, Guidance Search, to derive the routes efficiently and effectively. Experiments on Gowalla check-in datasets with user study show the promising performance of our proposed route recommendation method.

In this extended abstract we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by presenting a benchmark set of domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. We conclude with a brief update on the latest ALE developments. All of the software, including the benchmark agents, is publicly available.

In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to explore subgraph features from multiple graph-views for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.

Microarray experiments are capable of measuring the expression level of thousands of genes simultaneously. Dealing with this enormous amount of information requires complex computation. Support Vector Machines (SVM) have been widely used with great efficiency to solve classification problems that have high dimension. In this sense, it is plausible to develop new feature selection strategies for microarray data that are associated with this type of classifier. Therefore, we propose, in this paper, a new method for feature selection based on an ordered search process to explore the space of possible subsets. The algorithm, called Admissible Ordered Search (AOS), uses as evaluation function the margin values estimated for each hypothesis by a SVM classifier. An important theoretical contribution of this paper is the development of the projected margin concept. This value is computed as the margin vector projection on a lower dimensional subspace and is used as an upper bound for the current value of the hypothesis in the search process. This enables great economy in runtime and consequently efficiency in the search process as a whole. The algorithm was tested using five different microarray data sets yielding superior results when compared to three representative feature selection methods.

The functional and the algebraic routing problem are generalizations of the shortest path problem. This paper shows that both problems are equivalent with respect to the concept of profile searches known from time-dependent routing. Because of this, it is possible to apply various shortest path algorithms to these routing problems. This is demonstrated using contraction hierarchies as an example. Furthermore, we show how to use Cousots' concept of abstract interpretation on these routing problems generalizing the idea of routing approximations, which can be used to find approximative solutions and even to improve the performance of exact queries. The focus of this paper lies on vehicle routing while both the functional and algebraic routing models were introduced in the context of internet routing. Due to our formal combination of both fields, new algorithms abound for various specialized vehicle routing problems. We consider two major examples, namely the time-dependent routing problem for public transportation and the energy-efficient routing problem for electric vehicles.

Many problems in computational sustainability involve constraints on connectivity. When designing a new wildlife corridor, we need it to be geographically connected. When planning the harvest of a forest, we need new areas to harvest to be connected to areas that have already been harvested so we can access them easily. And when town planning, we need to connect new homes to the existing utility infrastructure. To reason about connectivity, we propose a new family of global connectivity constraints. We identify when these constraints can be propagated tractably, and give some efficient, typically linear time propagators for when this is the case. We report results on several benchmark problems which demonstrate the efficiency of our propagation algorithms and the promise offered by reasoning globally about connectivity.

The preferences of users are important in route search and planning. For example, when a user plans a trip within a city, their preferences can be expressed as keywords shopping mall, restaurant, and museum, with weights 0.5, 0.4, and 0.1, respectively. The resulting route should best satisfy their weighted preferences. In this paper, we take into account the weighted user preferences in route search, and present a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint. We prove that this problem is NP-hard. To solve this complex problem, we pro- pose an optimal route search based on an A* variant for which we have defined an admissible heuristic function. The experiments conducted on real-world datasets demonstrate both the efficiency and accu- racy of our proposed algorithms.

Influence maximization plays a key role in social network viral marketing. Although the problem has been widely studied, it is still challenging to estimate influence spread in big networks with hundreds of millions of nodes. Existing heuristic algorithms and greedy algorithms incur heavy computation cost in big networks and are incapable of processing dynamic network structures. In this paper, we propose an incremental algorithm for influence spread estimation in big networks. The incremental algorithm breaks down big networks into small subgraphs ad continuously estimate influence spread on these subgraphs as data streams. The challenge of the incremental algorithm is that subgraphs derived from a big network are not independent and MC simulations on each subgraph (defined as snapshots) may conflict with each other. In this paper, we assume that different combinations of MC simulations on subgraphs on subgraphs generate independent samples. In so doing, the incremental algorithm on streaming subgraphs can estimate influence spread with fewer simulations. Experimental results demonstrates the performance of the proposed algorithm.