This paper examines two-sided matching with budget constraints where one side (a firm or hospital) can make monetary transfers (offer wages) to the other (a worker or doctor). In a standard model, while multiple doctors can be matched to a single hospital, a hospital has a maximum quota; thus, the number of doctors assigned to a hospital cannot exceed a certain limit. In our model, in contrast, a hospital has a fixed budget; that is, the total amount of wages allocated by each hospital to doctors is constrained. With budget constraints, stable matchings may fail to exist and checking for the existence is hard. To deal with the nonexistence of stable matchings, we extend the "matching with contracts" model of Hatfield and Milgrom so that it deals with approximately stable matchings where each of the hospitals' utilities after deviation can increase by a factor up to a certain amount. We then propose two novel mechanisms that efficiently return a stable matching that exactly satisfies the budget constraints. Specifically, by sacrificing strategy-proofness, our first mechanism achieves the best possible bound. We also explore a special case on which a simple mechanism is strategy-proof for doctors, while maintaining the best possible bound of the general case.

The optimal pricing problem is a fundamental problem that arises in combinatorial auctions. Suppose that there is one seller who has indivisible items and multiple buyers who want to purchase a combination of the items. The seller wants to sell his items for the highest possible prices, and each buyer wants to maximize his utility (i.e., valuation minus payment) as long as his payment does not exceed his budget. The optimal pricing problem seeks a price of each item and an assignment of items to buyers such that every buyer achieves the maximum utility under the prices. The goal of the problem is to maximize the total payment from buyers. In this paper, we consider the case that the valuations are submodular. We show that the problem is computationally hard even if there exists only one buyer. Then we propose approximation algorithms for the unlimited budget case. We also extend the algorithm for the limited budget case when there exists one buyer and multiple buyers collaborate with each other.

In contrast, Inverse reinforcement learning (IRL), inverse optimal control, a discrete approach guarantees global optimality once and imitation learning(Ng and Russell 2000; Abbeel proper discrete state space is given, hence it is more suitable and Ng 2004) are modeling frameworks for acquiring rewards for driving behavior modeling. In a discrete approach, (or cost) of a certain environment by using the optimal the calculation cost of MaxEnt IRL is O( S A), where S path under a possibly different environment as training is the number of states and A is the number of actions data. In particular, in human behavior modeling, it is (Ziebart and others 2008). That is, the key for fast prediction shown that human-centered rewards can be obtained with is suppressing the increase of S depending on dimensions maximum entropy inverse reinforcement learning (MaxEnt and preparing a necessary and sufficient action set, A, IRL)(Ziebart and others 2008), which allows suboptimal for representing driving behavior. As examples of existing training data (Huang et al. 2015; Vernaza and Bagnell 2012; discretization schemes, there are mesh grid representation Dragan and Srinivasa 2012; Walker, Gupta, and Hebert (Shimosaka, Kaneko, and Nishi 2014) and random graph 2014). For instance, Ziebart et al. (Ziebart et al. 2008) modeled based representation connected with neighbors (Byravan et the driving behavior of expert taxi drivers and enabled al. 2015). In these approaches, however, A for general dynamic driving behavior prediction based on the experts' very own systems is not trivial. This is because neighbors on experience or knowledge. MaxEnt IRL based driving behavior state space defined by Euclidean distance do not necessarily prediction, which balances safety, comfort, and economic correspond to the transition area of general dynamics performance, is very promising.

Toward constructing a multimodal conversation agentsystem which can be used to interview elderly patients with dementia, we propose a turn taking mechanism based on recognition of the subjects attitude as to whether the subject has (or relinquish) the right to speak. A key strategy in the recognition task is to extract features from pausing behavior in subject’s spontaneous speech and to fuse multimodal signals (gaze, head motion, and speech). In this paper, we focus on evaluation of the recognition module used in guiding turn taking. To evaluate it, we collect multimodal data corpus from 42 dyadic conversations between subjects with dementia and the virtual agent we have developed as a prototype and annotate subject’s multimodal data manually. In experiments, we validate recognition models trained multimodal dataset by machine learning methods.Experimental results shows that pause features are effective to improve the attitude recognition accuracy and the accuracy is improved up to 88%.

This paper presents a novel framework for online incremental place recognition in an indoor environment. The framework addresses the scenario in which scene images are gradually obtained during long-term operation in the real-world indoor environment. Multiple users may interact with the classification system and confirm either current or past prediction results; the system then immediately updates itself to improve the classification system. This framework is based on the proposed \emph{n}-value self-organizing and incremental neural network (\emph{n}-SOINN), which has been derived by modifying the original SOINN to be appropriate for use in scene recognition. The evaluation was performed on the standard MIT 67-category indoor scene dataset and shows that the proposed framework achieves the same accuracy as that of the state-of-the-art offline method, while the computation time of the proposed framework is significantly faster and fully incremental update is allowed. Additionally, a small extra set of training samples is incrementally given to the system to simulate the incremental learning situation. The result shows that the proposed framework can leverage such additional samples and achieve the state-of-the-art result.

The increasing availability of “utility computing” resources such as clouds, grids, and massively parallel shared clusters can provide practically unlimited processing and memory capacity on demand, at some cost per unit of resource usage. This requires a new perspective in the design and evaluation of parallel search algorithms. Previous work in parallel search implicitly assumed ownership of a cluster with a static amount of CPU cores and RAM, and emphasized wallclock runtime. With utility computing resources, trade-offs between performance and monetary costs must be considered. This paper considers dynamically increasing the usage of utility computing resources until a problem is solved. Efficient resource allocation policies are analyzed in comparison with an optimal allocation strategy. We evaluate our iterative allocation strategy by applying it to the HDA* parallel search algorithm. The experimental results validate our theoretical predictions. They show that, in practice, the costs incurred by iterative allocation are reasonably close to an optimal (but a priori unknown) policy, and are significantly better than the worst-case analytical bounds.

Greedy best-first search (GBFS) is a popular and effective algorithm in satisficing planning and is incorporated into high-performance planners. GBFS in planning decides its search direction with automatically generated heuristic functions. However, if the heuristic functions evaluate nodes inaccurately, GBFS may be misled into a valueless search direction, thus resulting in performance degradation. This paper presents a simple but effective algorithm considering a diversity of search directions to avoid the errors of heuristic information. Experimental results in solving a variety of planning problems show that our approach is successful.

Methods for estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and conditional probability estimation. In this paper, we propose a new density-ratio estimator which incorporates dimensionality reduction into the density-ratio estimation procedure. Through experiments, the proposed method is shown to compare favorably with existing density-ratio estimators in terms of both accuracy and computational costs.

The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model through minimization of an estimator of the squared-loss mutual information between inputs and residuals. A notable advantage of LSIR over existing approaches is that tuning parameters such as the kernel width and the regularization parameter can be naturally optimized by cross-validation, allowing us to avoid overfitting in a data-dependent fashion. Through experiments with real-world datasets, we show that LSIR compares favorably with the state-of-the-art causal inference method.

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 parallel algorithms for optimal sequential planning, with an emphasis on exploiting distributed memory computing clusters.  In particular, we focus on an approach which 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 the optimal sequential version of the Fast Downward planner.  The scaling behavior of the algorithm is evaluated experimentally on clusters using up to 128 processors, a significant increase compared to previous work in parallelizing planners.  We show that this approach scales well, allowing us to effectively utilize the large amount of distributed memory to optimally solve problems which require hundreds of gigabytes of RAM to solve. We also show that this approach scales  well for a single, shared-memory multicore machine.