An optimization variant of a problem of path planning for multiple robots is addressed in this work. The task is to find spatial-temporal path for each robot of a group of robots such that each robot can reach its destination by navigating through these paths. In the optimization variant of the problem, there is an additional requirement that the makespan of the solution must be as small as possible. A proof of the claim that optimal path planning for multiple robots is NP‑complete is sketched in this short paper.

In several application areas for Planning, in particular helping with the creation of new processes in Business Process Management (BPM), a major obstacle lies in the modeling. Obtaining a suitable model to plan with is often prohibitively complicated and/or costly. Our core observation in this work is that, for software-architectural purposes, SAP is already using a model that is essentially a variant of PDDL. That model describes the behavior of Business Objects, in terms of status variables and how they are affected by system transactions. We show herein that one can leverage the model to obtain (a) a promising BPM planning application which incurs hardly any modeling costs, and (b) an interesting planning benchmark. We design a suitable planning formalism and an adaptation of FF, and we perform large-scale experiments. Our prototype is part of a research extension to the SAP NetWeaver platform.

People constantly apply acquired knowledge to new learning tasks, but machines almost never do. Research on transfer learning attempts to address this dissimilarity. Working within this area, we report on a procedure that learns and transfers constraints in the context of inductive process modeling, which we review. After discussing the role of constraints in model induction, we describe the learning method, MISC, and introduce our metrics for assessing the cost and benefit of transferred knowledge. The reported results suggest that cross-domain transfer is beneficial in the scenarios that we investigated, lending further evidence that this strategy is a broadly effective means for increasing the efficiency of learning systems. We conclude by discussing the aspects of inductive process modeling that encourage effective transfer, by reviewing related strategies, and by describing future research plans for constraint induction and transfer learning.

Developing a robust, flexible, closed-loop walking algorithm for a humanoid robot is a challenging task due to the complex dynamics of the general biped walk. Common analytical approaches to biped walk use simplified models of the physical reality. Such approaches are partially successful as they lead to failures of the robot walk in terms of unavoidable falls. Instead of further refining the analytical models, in this work we investigate the use of human corrective demonstrations, as we realize that a human can visually detect when the robot may be falling. We contribute a two-phase biped walk learning approach, which we experiment on the Aldebaran NAO humanoid robot. In the first phase, the robot walks following an analytical simplified walk algorithm, which is used as a black box, and we identify and save a walk cycle as joint motion commands. We then show how the robot can repeatedly and successfully play back the recorded motion cycle, even if in open-loop. In the second phase, we create a closed-loop walk by modifying the recorded walk cycle to respond to sensory data. The algorithm learns joint movement corrections to the open-loop walk based on the corrective feedback provided by a human, and on the sensory data, while walking autonomously. In our experimental results, we show that the learned closed-loop walking policy outperforms a hand-tuned closed-loop policy and the open-loop playback walk, in terms of the distance traveled by the robot without falling.

Current practice in Wilderness Search and Rescue (WiSAR) is analogous to an intelligent system designed to gather and analyze information to find missing persons in remote areas. The system consists of multiple parts - various tools for information management (maps, GPS, etc) distributed across personnel with different skills and responsibilities. Introducing a camera-equipped mini-UAV into this task requires autonomy and information technology that itself is an integrated intelligent system to be used by a sub-team that must be integrated into the overall intelligent system. In this paper, we identify key elements of the integration challenges along two dimensions: (a) attributes of intelligent system and (b) scale, meaning individual or group. We then present component technology that offload or supplement many responsibilities to autonomous systems, and finally describe how autonomy and information are integrated into user interfaces to better support distributed search across time and space. The integrated system was demoed for Utah County Search and Rescue personnel. A real searcher flew the UAV after minimal training and successfully located the simulated missing person in a wilderness area.

Non-metric distances are often more reasonable compared with metric ones in terms of consistency with human perceptions. However, existing locality-sensitive hashing (LSH) algorithms can only support data which are gauged with metrics. In this paper we propose a novel locality-sensitive hashing algorithm targeting such non-metric data. Data in original feature space are embedded into an implicit reproducing kernel Krein space and then hashed to obtain binary bits. Here we utilize the norm-keeping property of p-stable functions to ensure that two data's collision probability reflects their non-metric distance in original feature space. We investigate various concrete examples to validate the proposed algorithm. Extensive empirical evaluations well illustrate its effectiveness in terms of accuracy and retrieval speedup.

Multiple Kernel Learning (MKL) can be formulated as a convex-concave minmax optimization problem, whose saddle point corresponds to the optimal solution to MKL. Most MKL methods employ the L1-norm simplex constraints on the combination weights of kernels, which therefore involves optimization of a non-smooth function of the kernel weights. These methods usually divide the optimization into two cycles: one cycle deals with the optimization on the kernel combination weights, and the other cycle updates the parameters of SVM. Despite the success of their efficiency, they tend to discard informative complementary kernels. To improve accuracy, we introduce smoothness to the optimization procedure. Furthermore, we transform the optimization into a single smooth convex optimization problem and employ the Nesterov’s method to efficiently solve the optimization problem. Experiments on benchmark data sets demonstrate that the proposed algorithm clearly improves current MKL methods in a number scenarios.

We explore the relationship between two approaches to rationalizing voting rules: the maximum likelihood estimation (MLE) framework originally suggested by Condorcet and recently studied by Conitzer, Rognlie, and Xia, and the distance rationalizability (DR) framework of Elkind, Faliszewski, and Slinko. The former views voting as an attempt to reconstruct the correct ordering of the candidates given noisy estimates (i.e., votes), while the latter explains voting as search for the nearest consensus outcome. We provide conditions under which an MLE interpretation of a voting rule coincides with its DR interpretation, and classify a number of classic voting rules, such as Kemeny, Plurality, Borda and Single Transferable Vote (STV), according to how well they fit each of these frameworks. The classification we obtain is more precise than the ones that result from using MLE or DR alone: indeed, we show that the MLE approach can be used to guide our search for a more refined notion of distance rationalizability and vice versa.

Top- k approximate querying on string collections is an important data analysis tool for many applications, and it has been exhaustively studied. However, the scale of the problem has increased dramatically because of the prevalence of the Web. In this paper, we aim to explore the efficient top- k similar string matching problem. Several efficient strategies are introduced, such as length aware and adaptive q -gram selection. We present a general q -gram based framework and propose two efficient algorithms based on the strategies introduced. Our techniques are experimentally evaluated on three real data sets and show a superior performance.

Crowd-sourcing is a recent framework in which human intelligence tasks are outsourced to a crowd of unknown people ("workers") as an open call (e.g., on Amazon's Mechanical Turk). Crowd-sourcing has become immensely popular with hoards of employers ("requesters"), who use it to solve a wide variety of jobs, such as dictation transcription, content screening, etc. In order to achieve quality results, requesters often subdivide a large task into a chain of bite-sized subtasks that are combined into a complex, iterative workflow in which workers check and improve each other's results. This paper raises an exciting question for AI — could an autonomous agent control these workflows without human intervention, yielding better results than today's state of the art, a fixed control program? We describe a planner, TurKontrol, that formulates workflow control as a decision-theoretic optimization problem, trading off the implicit quality of a solution artifact against the cost for workers to achieve it. We lay the mathematical framework to govern the various decisions at each point in a popular class of workflows. Based on our analysis we implement the workflow control algorithm and present experiments demonstrating that TurKontrol obtains much higher utilities than popular fixed policies.