Current methods for multi-AUV cooperation suffer in low communication environments. State of the art methods employ auctioneering or planning to determine a single AUV'task. These systems require communication to update models of teammates and tasks for efficient task selection. Most strategies assume a teammate is inoperable if a communication timeout is reached which reduces overall team efficiency. Including teammate prediction has been shown to mitigate efficiency degeneration due to low communication. However, there is no verification of a predicted teammate's task other than through eventual communication. A possible verification tool is behavior recognition. Current behavior recognition utilizes either overhead sensors or post mission analysis to track robot trajectories in order to infer their internal state. A system in which an AUV is capable of sensing a teammate, for example through a forward-looking sonar, and deducing it's behavior along with contextual information, such as location, will enable an AUV to determine that teammate's current task in the overall mission. This will allow for an accurate update of that teammate's model allowing the AUV to more efficiently determine its own next task rather than relying only on communication. This position paper posits that multi-AUV cooperation efficiency will improve in low communication environments with the combination of robust teammate prediction along with verification using behavior recognition.

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing.

In Hypertable ranges of table data are stored and accessed on different nodes and allows for flexible management of the underlying hardware. Overall performance is sensitive to the balance of range load across the cluster. The project developers aim to create a simple interface to allow researchers to design experimental load balancing strategies that incorporate machine learning and optimization. This paper specifies the load balancing problem and introduces it as a challenge problem for AI and machine learning.

This paper proposes a new challenging optimization problem, called COOLING-AWARE WORKLOADPLACEMENT PROBLEM, that looks for a workload placement that optimizes the overall data center power consumption given by the sum of the server power consumption and of the computer room air conditioner power consumption. We formulate CWPP as a Mixed Integer Non Linear Problem using a cross-interferencematrix that links the workload placement to the cold airtemperature. Since state-of-the-art Mixed Integer Non Linear solvers can solve to optimality only the smallest instances, we devised two heuristics to obtain good feasible solutions: (i) a heuristic algorithm based on an integer linear relaxation of the problem, and (ii) a VariableNeighborhood Search algorithm. Both heuristic algorithms are evaluated against the best lower bounds obtained with a Mixed Integer Non Linear solver. Preliminary computational results show that both heuristics provide solutions that have a small percentage gap from the optimal solutions.

Malfunctioning HVAC equipment in commercial buildings wastes between 15% and 30% of energy. Many diagnosis approaches tackle this problem, but they either suffer from a lack of detailed fault information or a lack of adaptability to different buildings and equipment. Clearly, especially in the light of an ever increasing amount of sensor data that is available in heavily metered smart buildings, easily adaptable self learning in-depth diagnosis approaches are needed. This paper addresses the challenges of developing such approaches and describes the contribution artificial intelligence techniques like transfer learning, ontologies, knowledge representation or diagnosis can make in overcoming these challenges.

This paper presents a novel scoring rule-based mechanism that encourages agents to produce costly estimates of future events and truthfully report them to a centre when the budget for payments to the agents is itself determined by their reports. This is applied to a model of aggregated demand prediction within a microgrid where, given estimates of future consumptions, an aggregator must optimally purchase electricity for a set of homes, each represented by self-interested, rational home agents. This in turn reduces the need for costly standby generation within the grid. The aggregator has prior information about the amount each home will consume, and determines the amount to pay each agent based on savings resulting from using the agents' reported information, over its own prior information. Agents use sensory information regarding their property and its occupants to generate these estimates, which they transmit to the aggregator using smart grid technology. The proposed mechanism is dominant strategy incentive compatible and empirical evaluation shows that it encourages agents to exert effort in producing precise estimates. We show that the mechanism is ex ante individually rational for the aggregator, and that it outperforms a simpler mechanism whereby savings are distributed evenly.

End-user programming raises the possibility that the people who know best what a software system should do will be able to customize, remedy original programming defects and adapt systems as requirements change. As computing increasingly enters the home and workplace, the need for such tools is high, but state of practice approaches offer very limited capability. We describe the Interactive Bootstrapped Learning (iBL) system which allows users to modify code by interactive teaching similar to human instruction. It builds on an earlier system focused on exploring how machine learning can be used to compensate for limited instructional content. iBL provides an end-to-end solution in which user-iBL dialog gradually refines a hypothesis about what transformation to a target code base will best achieve user intent. The approach integrates elements of many AI technologies including machine learning, dialog management, AI planning and automated model construction.

Humans are confronted with an increasingly complex array of ingestion substances and dietary choices that influence health and well being. However, even with strong medical evidence that clearly links ingestion strategies and heath consequences, the general public struggles to make health-optimizing ingestion decisions. Based on our literature review, we delineate a typology of barriers to formulating health-optimizing ingestion strategies. We propose that the introduction of artificial intelligence (AI) as “decision management” (AI-DM) technology into the ingestion decision-making network would increase the likelihood of more predictable and optimized health outcomes. Also, we delineate the key informational constituencies needed to enable a comprehensive and effective AI-DM system. While no author has yet proposed AI in the particular context discussed in this paper, the theoretical and empirical literature suggests that this might be possible. We conclude by discussing areas for additional research.

We present a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user’s state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin or end tasks. The objective of the system is to help the user maintain a daily schedule of activities while minimizing interruptions from questions or prompts. The model is built upon an option-based hierarchical POMDP. Options can be programmed and customized to specify complex routines for prompting or questioning. Novel aspects of the model include (1) the introduction of adaptive options, which employ a lightweight user model and are able to provide near-optimal performance with little exploration; (2) a restricted-inquiry dual-control algorithm that can appeal for help from the user when sensor data is ambiguous; and (3) a combined filtering / most likely-sequence algorithm for activities determining the beginning and ending time points of the user’s activities. Experiments show that each of these features contributes to the robustness of the model.

This paper considers a setting where a single ``leadership agent'' intervenes in a multi-agent system through actions that (perhaps subtly) change the dynamics of the system. We describe a number of forms this intervention can take and compare these situations to settings in previous work. We identify two important effects of leadership: faster system convergence, and convergence to a better equilibrium. Empirically, we first explore these properties in leadership of algorithms engaged in classical 2-player games. We then apply this general framework to the leadership of a super-peer file-sharing network. In these experiments the network contains some agents that make locally greedy decisions that hamper the network as a whole. We show that a leader acting based on a more global criteria can push the system to a better equilibrium point as well as speeding up convergence. We also show how a mathematical approximation of such super-peer networks can be used to aid a leader in determining a minimum-cost intervention strategy.