In this paper, a practical example of implemented high abstraction-level control of mobile robot is presented. A method to represent abstract plans is shown along with a mechanism to schedule the actions within the plans for concurrent execution. Furthermore, a mechanism to consider contingencies and dynamic environment is explained.

This paper shows an example application of the LTLMoP mission planning toolkit, in which an Aldebaran Nao and an iRobot Create each play a very basic game of hide-and-seek.

The use of automated action planning techniques is essential for efficient mission execution of mobile robots. However, a tremendous effort is needed to represent planning problem domains realistically to meet the real-world constraints. Therefore, there is another source of uncertainty for mobile robot systems due to the impossibility of perfectly representing action representations (e.g., preconditions and effects) in all circumstances. When domain representations are not complete, a planner may not be capable of constructing a valid plan for dynamic events even when it is possible. This research focuses on a generic domain update method to construct alternative plans against real-time execution failures which are detected either during runtime or earlier by a plan simulation process. Based on the updated domain representations, a new executable plan is constructed even when the outcomes of existing operators are not completely known in advance or valid plans are not possible with the existing representation of the domain. A failure resolution scenario is given in the realistic Webots simulator with mobile robots. Since TLPlan is used as the base temporal planner, makespan optimization is achieved with the available knowledge of the robots.

We present an original integration of high level planning and execution with incoming perceptual information from vision, SLAM, topological map segmentation and dialogue. The task of the robot system, implementing the integrated model, is to explore unknown areas and report detected objects to an operator, by speaking loudly. The knowledge base of the planner maintains a graph-based representation of the metric map that is dynamically constructed via an unsupervised topological segmentation method, and augmented with information about the type and position of detected objects, within the map, such as cars or containers. According to this knowledge the cognitive robot can infer strategies in so generating parametric plans that are instantiated from the perceptual processes. Finally, a model-based approach for the execution and control of the robot system is proposed to monitor, concurrently, the low level status of the system and the execution of the activities, in order to achieve the goal, instructed by the operator.

The task of coordinating hundreds of mobile robots in one of Kiva System's warehouses presents many challenging multi-agent resource allocation problems. The resources include things like inventory, open orders, small shelving units, and the robots themselves. The types of resources can be classified by whether they are consumable, recycled, or scheduled. Further, the global optimization problem can be broken down into more manageable sub-problems, some of which map to (hard) versions of well known computational problems, but with a dynamic, temporal twist.

Mainly, we need kinds of virtualization technologies to offer on-demand to identify what parts of the model are really important and computing resources. There is widespread consensus that what other parts are secondary. Then, we would like to study the Future Internet will be heavily based on some kind of the scalability of the current models and identify the current successful Cloud technology. However, to master the deployment limits. Therefore, we propose to consider all existing of Cloud-based infrastructures, some hard scientific CP models in order to answer to these questions.

Data centers today are heterogeneous: they have servers from multiple generations and multiple vendors; server machines have multiple cores that are capable of running at difference speeds, and some have general purpose graphics processing units (GPGPU). Hardware trends indicate that future processors will have heterogeneous cores with different speeds and capabilities. This environment enables new advances in power saving and application optimization. It also poses new challenges, as current systems software is ill-suited for heterogeneity. In this position paper, we focus on interactive applications and outline some of the techniques to embrace heterogeneity. We show that heterogeneity can be exploited to deliver interactive services in an energy-efficient manner. For example, our initial study suggests that neither high-end nor low-end servers alone are very effective in servicing a realistic workload, which typically has requests with varying service demands. High-end servers achieve good throughput but the energy costs are high. Low-end servers are energy-efficient for short requests, but they may not be able to serve long requests at the desired quality of service. In this work, we show that a heterogeneous system can be a better choice than an equivalent homogeneous system to deliver interactive services in a cost-effective manner, transforming heterogeneity from a resource management nightmare to an asset. We highlight some of the challenges and opportunities and the role of AI and machine learning techniques for hosting large interactive services in data centers.

Despite a dramatic growth of power consumption inhouseholds, less attention has been paid to monitoring,analyzing and predicting energy usage. In this paper,we propose a framework to mine raw energy data bytransforming time series energy data into a symbol se-quence, and then extend a suffix tree data structure asan efficient representation to analyze global structuralpatterns. Then, we use a clustering algorithm to detectenergy pattern outliers which are far from their clustercentroids. To validate our approach, we use real powerdata collected from a smart apartment testbed duringtwo months.

Adversarial behavioral has been observed in many different contexts. In this paper we address the problem of adversarial behavior in the context of clandestine networks. We use data from a massively multiplayer online role playing game to illustrate the behavioral and structural signatures of deviant players change over time as a response to "policing" activities of the game administrators. Preliminary results show that the behavior of the deviant players and their affiliates show co-evolutionary behavior and the timespan within the game can be divided into different epochs based on their behaviors. Feature sets derived from these results can be used for better predictive machine learning models for detecting deviants in clandestine networks.

The goal for attackers is to move from one side of the graph to the Border security is a key element of national security policy other (represented by sets of source and target nodes); this for any sovereign nation. In the United States, the Border represents a typical scenario of crossing an open region from Patrol deploys thousands of agents integrated with technology one side of the border to destination points in the interior of (e.g., vehicles, cameras, sensors) and infrastructure the county. The paths between the source and target nodes (e.g., fences, checkpoints) to prevent illegal entry of people may represent major or minor roads, or paths suitable for and goods into the country along vast land borders with travel on foot. We use weights on the edges to represent Canada and Mexico. The problem of border security is incredibly the relative speed/cost of transit on the different paths (for complex, due to the diversity and volume of illegal example, it may be must slower and more difficult to use activity that must be controlled, the variety of resources that a foot path than a major highway). Nodes may represent can be deployed to secure the border, and the differences in intersections, checkpoints, or other important waypoints.