Adi Botea

Optimal Reconfiguration for Supply Restoration With Informed A

Reconfiguration of radial distribution networks is the basis of supply restoration after faults and of load balancing and loss minimization. The ability to automatically reconfigure the network quickly and efficiently is a key feature of autonomous and self-healing networks, an important part of the future vision of smart grids. We address the reconfiguration problem for outage recovery, where the cost of the switching actions dominates the overall cost: when the network reverts to its normal configuration relatively quickly, the electricity loss and the load imbalance in a temporary suboptimal configuration are of minor importance. Finding optimal feeder configurations under most optimality criteria is a difficult optimization problem. All known complete optimal algorithms require an exponential time in the network size in the worst case, and cannot be guaranteed to scale up to arbitrarily large networks. Hence most works on reconfiguration use heuristic approaches that can deliver solutions but cannot guarantee optimality. These approaches include local search, such as tabu search, and evolutionary algorithms. We propose using optimal informed search algorithms in the A family, introduce admissible heuristics for reconfiguration, and demonstrate empirically the efficiency of our approach. Combining A with admissible cost lower bounds guarantees that reconfiguration plans are optimal in terms of switching action costs.

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We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems.

Search

Large-scale, parallel clusters composed of commodity processors are increasingly available, enabling the use of vast processing capabilities and distributed RAM to solve hard search problems. We investigate Hash-Distributed A^@? (HDA^@?), a simple approach to parallel best-first search that asynchronously distributes and schedules work among processors based on a hash function of the search state. We use this approach to parallelize the A^@? algorithm in an optimal sequential version of the Fast Downward planner, as well as a 24-puzzle solver. The scaling behavior of HDA^@? is evaluated experimentally on a shared memory, multicore machine with 8 cores, a cluster of commodity machines using up to 64 cores, and large-scale high-performance clusters, using up to 2400 processors. We show that this approach scales well, allowing the effective utilization of large amounts of distributed memory to optimally solve problems which require terabytes of RAM. We also compare HDA^@? to Transposition-table Driven Scheduling (TDS), a hash-based parallelization of IDA^@?, and show that, in planning, HDA^@? significantly outperforms TDS. A simple hybrid which combines HDA^@? and TDS to exploit strengths of both algorithms is proposed and evaluated.

The 2014 International Planning Competition: Progress and Trends

Building efficient and sustainable transportation systems is a key challenge for accommodating the fast-increasing population living in cities. Lack of efficiency in transportation networks typically arises from uncertainty, e.g., about the availability of resources (such as parking lots or bicycles in bike sharing systems), or the exogenous factors affecting their demand (such as weather or the time of the day). In this paper, we present a class of algorithms which use Generalized Additive Models (GAMs) for demand and availability prediction on various time scales. In contrast to existing methods, exogenous effects can be explicitly factored into the models, resulting in significant gains in terms of prediction accuracy. Another advantage of our approach is that it estimates the distribution of the waiting time for the next available bike/parking lot if the current availability is zero. We showcase how this additional information can be used as part of personal uncertainty-aware journey planners which allow users to choose from multiple routes according to their time constraints.

Evaluation of a simple, scalable, parallel best-first search strategy

This chapter arises from the discussions of an experienced international group of researchers interested in the potential for creative application of algorithms for searching finite discrete graphs, which have been highly successful in a wide range of application areas, to address a broad range of problems arising in video games. The chapter first summarises the state of the art in search algorithms for games. It then considers the challenges in implementing these algorithms in video games (particularly real time strategy and first-person games) and ways of creating searchable discrete representations of video game decisions (for example as state-action graphs). Finally the chapter looks forward to promising techniques which might bring some of the success achieved in games such as Go and Chess, to real-time video games. For simplicity, we will consider primarily the objective of maximising playing strength, and consider games where this is a challenging task, which results in interesting gameplay.

Uncertainty in urban mobility: Predicting waiting times for shared bicycles and parking lots

Knowledge on the Web relies heavily on multi-relational representations, such as RDF and Schema.org. Automatically extracting knowledge from documents and linking existing databases are common approaches to construct multi-relational data. Complementary to such approaches, there is still a strong demand for manually encoding human expert knowledge. For example, human annotation is necessary for constructing a common-sense knowledge base, which stores facts implicitly shared in a community, because such knowledge rarely appears in documents. As human annotation is both tedious and costly, an important research challenge is how to best use limited human resources, whiles maximizing the quality of the resulting dataset. In this paper, we formalize the problem of dataset construction as active learning problems and present the Active Multi-relational Data Construction (AMDC) method. AMDC repeatedly interleaves multi-relational learning and expert input acquisition, allowing us to acquire helpful labels for data construction. Experiments on real datasets demonstrate that our solution increases the number of positive triples by a factor of 2.28 to 17.0, and that the predictive performance of the multi-relational model in AMDC achieves the highest or comparable to the best performance throughout the data construction process.

Search in Real-Time Video Games

In moving target search, the objective is to guide a hunter agent to catch a moving prey. Even though in game applications maps are always available at developing time, current approaches to moving target search do not exploit preprocessing to improve search performance. In this paper, we propose MtsCopa, an algorithm that exploits precomputed information in the form of compressed path databases (CPDs), and that is able to guide a hunter agent in both known and partially known terrain. CPDs have previously been used in standard, fixed-target pathfinding but had not been used in the context of moving target search. We evaluated MtsCopa over standard game maps. Our speed results are orders of magnitude better than current state of the art. The time per individual move is improved, which is important in real-time search scenarios, where the time available to make a move is limited. Compared to state of the art, the number of hunter moves is often better and otherwise comparable, since CPDs provide optimal moves along shortest paths. Compared to previous successful methods, such as I-ARA*, our method is simple to understand and implement. In addition, we prove MtsCopa always guides the agent to catch the prey when possible.

Active Learning for Multi-relational Data Construction

The aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.

Fast Algorithm for Catching a Prey Quickly in Known and Partially Known Game Maps

We introduce a novel approach to Compressed Path Databases, space efficient oracles used to very quickly identify the first edge on a shortest path. Our algorithm achieves query running times on the 100 nanosecond scale, being significantly faster than state-of-the-art first-move oracles from the literature. Space consumption is competitive, due to a compression approach that rearranges rows and columns in a first-move matrix and then performs run length encoding (RLE) on the contents of the matrix. One variant of our implemented system was, by a convincing margin, the fastest entry in the 2014 Grid-Based Path Planning Competition. n nWe give a first tractability analysis for the compression scheme used by our algorithm. We study the complexity of computing a database of minimum size for general directed and undirected graphs. We find that in both cases the problem is NP-complete. We also show that, for graphs which can be decomposed along articulation points, the problem can be decomposed into independent parts, with a corresponding reduction in its level of difficulty. In particular, this leads to simple and tractable algorithms with linear running time which yield optimal compression results for trees.

Managing travels with PETRA: The Rome use case

Recommender systems can be employed to assist users in complex decision making processes. This paper presents a multi-criteria housing recommender system which takes into account not just features of a home, such as rent, but also the transportation links to user specified locations. First, we describe an efficient multi-hop journey time calculator. Second, we introduce a mechanism to find the optimal solutions for multi-criteria evaluation, where a balanced trade-off between the target goals is found. Finally, we present a user study to demonstrate the potential of such a system.