Last-mile transportation (LMT) refers to any service that moves passengers from a hub of mass transportation (MT), such as air, boat, bus, or train, to destinations, such as a home or an office. In this paper, we introduce the problem of scheduling passengers jointly on MT and LMT services, with passengers sharing a car, van, or autonomous pod of limited capacity for LMT. Passenger itineraries are determined so as to minimize total transit time for all passengers, with each passenger arriving at the destination within a specified time window. The transit time includes the time spent traveling through both services and, possibly, waiting time for transferring between the services. We provide an integer linear programming (ILP) formulation for this problem. Since the ILMTP, is NP-hard and problem instances of practical size are often difficult to solve, we study a restricted version where MT trips are uniform, all passengers have time windows of a common size, and LMT vehicles visit one destination per trip. We prove that there is an optimal solution that sorts and groups passengers by their deadlines and, based on this result, we propose a constructive grouping heuristic and local search operators to generate high-quality solutions. The resulting groups are optimally scheduled in a few seconds using another ILP formulation. Numerical results indicate that the solutions obtained by this heuristic are often close to optimal %, even when multiple destinations are allowed per group, and that warm-starting the ILP solver with such solutions decreases the overall computational times significantly.

Internet of things (IoT), from heart monitoring implants to home heating control systems, are becoming an integral part of our daily lives. We expect these technologies to become smarter, able to autonomously reason, act, and communicate with other entities in the environment and act to achieve shared goals. To realize the full potential of these systems, we need to understand the mechanisms that allow multiple agents to effectively operate in changing and uncertain environments. This paper presents a framework that postulates that optimal multi-agent systems achieve adaptive behaviors by minimizing the team’s free energy, where energy minimization process consists of incremental perception (inference) and control (action) phases. We discuss instantiation of this mechanism for a problem of joint distributed decision making, provide the concomitant abstractions and computational mechanisms, and present experimental evidence that energy-based agent teams significantly outperform utility-based teams. We discuss different adaptation mechanisms and scales, explain agent interdependencies produced by energy-based modeling, and look at the role of learning in the adaptation process. We hypothesize that to efficiently operate in uncertain and changing environments, IoT devices must not only maintain enough intelligence to perceive and act locally, but also possess team-level adaptation primitives. We posit that such primitives must embody energy-minimizing mechanisms but can be locally defined without the need for agents to possess global team-level objectives or constraints.

We propose a tensor-based approach to analyze multi-dimensional data describing sample subjects. It simultaneously discovers patterns in features and reveals past temporal points that have impact on current outcomes. The model coefficient, a k-mode tensor, is decomposed into a summation of k tensors of the same dimension. To accomplish feature selection, we introduce the tensor '"atent L F,1 norm" as a grouped penalty in our formulation. Furthermore, the proposed model takes into account within-subject correlations by developing a tensor-based quadratic inference function. We provide an asymptotic analysis of our model when the sample size approaches to infinity. To solve the corresponding optimization problem, we develop a linearized block coordinate descent algorithm and prove its convergence for a fixed sample size. Computational results on synthetic datasets and real-file fMRI and EEG problems demonstrate the superior performance of the proposed approach over existing techniques.

The CP conference is the annual international conference on constraint programming. It is concerned with all aspects of computing with constraints, including theory, algorithms, environments, languages, models, systems, and applications such as decision-making, resource allocation, scheduling, configuration, and planning. The CP community is very keen to ensure it remains open to interdisciplinary research at the intersection between constraint programming and related fields. Hence, in addition to the usual technical and application tracks, the CP 2016 conference featured thematic tracks: Computational Sustainability, CP and Biology, Preferences, Social Choice and Optimization, and Testing and Verification. In this overview, we highlight several remarkable papers that have been selected by the senior program committee and papers with the most innovative methods and techniques, and a very high potential for applications (in our opinion).

We have implemented a simulation of a robot functioning in the domain of eldercare whose behavior is completely determined by an ethical principle. Using a subset of the perceptions and duties that will be required of such a robot, this simulation demonstrates selection of ethically preferable actions in real time using a case-supported principle-based paradigm. We believe that this work could serve as the basis for ensuring that the behavior of all eldercare robots that are created in the future will be ethically justifiable. Further, we believe that the methods used in this project can be employed in other domains as well, to ensure that the robots that humans interact with in these domains will behave ethically.

An integral part of human-robot collaboration is the ability ofthe robot to understand and predict human motion. Predicting what the human collaborator will do next is very useful in planning the robot’s response. In this paper, an algorithm for early detection and prediction of human activities is presented. For a given sequential task composed of many steps, a long short-term memory (LSTM) recurrent neural network (RNN) model is trained to learn the underlying sequence of steps. The trained network is then used to make predictions about the subsequent steps the human is about to carry out. The prediction of next steps requires information about the current step that is being carried out. The steps are inferred by observing the motion trajectories of the human arm and predicting where the human is reaching. The trajectories of the arm motion are modeled by using a dynamical system with contracting behavior towards the object. A neural network (NN) is used to learn the dynamics under the contraction analysis constraints. An interacting multiple model (IMM) framework is used for the early prediction of the goal locations of reaching motions. Since humans tend to look in the direction of the object they are reaching for, the prior probabilities of the models are calculated based on the human eye gaze. Experimental results based on an audio amplifier circuit assembly task are used to validate the proposed algorithm.

We advocate a case-supported principle-based behavior paradigm coupled with the Fractal robot architecture as a means to control an eldercare robot. The most ethically preferable action at any given moment is determined using a principle, abstracted from cases where a consensus of ethicists exists.

Collective social and behavioral information commonly exists in nature. There is a widespread intuitive sense that the characteristics of these social and behavioral information are to some extend related to the themes (or semantics) of the activities or targets. In this paper, we explicitly validate the interplay of collective social behavioral information and group themes using a large scale real dataset of online groups, and demonstrate the possibility of perceiving group themes from collective social and behavioral information. We propose a REgularized miXEd Regression (REXER) model based on matrix factorization to infer hierarchical semantics (including both group category and group labels) from collective social and behavioral information of group members. We extensively evaluate the proposed method in a large scale real online group dataset. For the prediction of group themes, the proposed REXER achieves satisfactory performances in various criterions. More specifically, we can predict the category of a group (among 6 categories) purely based on the collective social and behavioral information of the group with the Precision@1 to be 55.16% , without any assistance from group labels or conversation contents. We also show, perhaps counterintuitively, that the collective social and behavioral information is more reliable than the titles and labels of groups for inferring the group categories.

Due to the simplicity and efficiency, many hashing methods have recently been developed for large-scale similarity search. Most of the existing hashing methods focus on mapping low-level features to binary codes, but neglect attributes that are commonly associated with data samples. Attribute data, such as image tag, product brand, and user profile, can represent human recognition better than low-level features. However, attributes have specific characteristics, including high-dimensional, sparse and categorical properties, which is hardly leveraged into the existing hashing learning frameworks. In this paper, we propose a hashing learning framework, Probabilistic Attributed Hashing (PAH), to integrate attributes with low-level features. The connections between attributes and low-level features are built through sharing a common set of latent binary variables, i.e. hash codes, through which attributes and features can complement each other. Finally, we develop an efficient iterative learning algorithm, which is generally feasible for large-scale applications. Extensive experiments and comparison study are conducted on two public datasets, i.e., DBLP and NUS-WIDE. The results clearly demonstrate that the proposed PAH method substantially outperforms the peer methods.

A paradigm of case-supported principle-based behavior (CPB) is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles needed for ethical guidance of the behavior of autonomous systems. Such principles help ensure the ethical behavior of complex and dynamic systems and further serve as a basis for justification of their actions as well as a control abstraction for managing unanticipated behavior. The requirements, methods, implementation, and evaluation components of the CPB paradigm are detailed.