Industry Track

Session A


  The trend towards renewable, decentralized, and highly fluctuating energy suppliers (e.g. photovoltaic, wind power, CHP)introduces a tremendous burden on the stability of futurepower grids. By adding sophisticated ICT and intelligentdevices, various Smart Grid initiatives work on concepts forintelligent power meters, peak load reductions, efficient balancing mechanisms, etc. As in the Smart Grid scenario datais inherently distributed over different, often non-cooperativeparties, mechanisms for efficient coordination of the suppliers, consumers and intermediators is required in order toensure global functioning of the power grid. In this paper,a highly flexible market platform is introduced for coordinating self-interested energy agents representing power suppliers, customers and prosumers. These energy agents implement a generic bidding strategy that can be governedby local policies. These policies declaratively represent userpreferences or constraints of the devices controlled by theagent. Efficient coordination between the agents is realizedthrough a market mechanism that incentivizes the agents toreveal their policies truthfully to the market. By knowingthe agent’s policies, an efficient solution for the overall system can be determined. As proof of concept implementationthe market platform D’ACCORD is presented that supportsvarious market structures ranging from a single local energyexchange to a hierarchical energy market structure. An Agent-based Market Platform for Smart Grids Steffen Lamparter, Silvio Becher, Jan-Gregor Fischer


  Wireless cognitive radio (CR) is a newly emerging paradigmthat attempts to opportunistically transmit in licensed frequencies, without affecting the pre-assigned users of thesebands. To enable this functionality, such a radio must predict its operational parameters, such as transmit power andspectrum. These tasks, collectively called spectrum management, is difficult to achieve in a dynamic distributed environment, in which CR users may only take local decisions, andreact to the environmental changes. In this paper, we introduce a multi-agent reinforcement learning approach basedspectrum management. Our approach uses value functionsto evaluate the desirability of choosing different transmission parameters, and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward. We then investigate various real-world scenarios, andcompare the communication performance using different setsof learning parameters. We also apply Kanerva-based function approximation to improve our approach’s ability to handle large cognitive radio networks and evaluate its effect oncommunication performance. We conclude that our reinforcement learning based spectrum management can significantly reduce the interference to the licensed users, whilemaintaining a high probability of successful transmissions ina cognitive radio ad hoc network. Spectrum Management of Cognitive Radio Using Multi-agent Reinforcement Learning Cheng Wu, Kaushik Chowdhury, Marco Di Felice, Waleed Meleis

Session B


  Commercial aviation transportation is on the rise and hasbecome a necessity in our increasingly global world. Thereis a societal demand for more options, more traffic, moreefficiency, while still maintaining safety in the airspace. Tomeet these demands the Next Generation Air Transportation System (NextGen) concept from NASA calls for technologies and systems offering increasing support from automated decision-aiding and optimization tools. Such systemsmust coordinate with the human operator to take advantage of the functions each can best perform: The automatedtools must be designed to support the optimal allocation oftasks (functions) between the system and the human operators using these systems. Preliminary function allocationmethods must be developed (and evaluated) that focus onthe NextGen Airportal challenges, given a flexible, changingConcept of Operations (ConOps).We have begun making steps toward this by leveragingwork in agents research (namely Adjustable Autonomy) inorder to allow function allocation to become more dynamicand adjust to the goals, demands, and constraints of thecurrent situation as it unfolds. In this paper we introduceDynamic Function Allocation Strategies (DFAS) that arenot static and singular, but rather are represented by allocation policies that vary over time and circumstances. TheNextGen aviation domain is a natural fit for agent basedsystems because of its inherently distributed nature and theneed for automated systems to coordinate on tasks mapswell to the adjustable autonomy problem. While current adjustable autonomy methods are applicable in this context,crucial extensions are needed to push the existing models tolarger numbers of human players, while maintaining criticaltiming. To this end, we have created an air traffic controlsystem that includes: (1) A simulation environment, (2) aDFAS algorithm for providing adjustable autonomy strategies and (3) the agents for executing the strategies and measuring system efficiency. We believe that our system is thefirst step towards showing the efficacy of agent supportedapproach to driving the dynamic roles across human operators and automated systems in the NextGen environment.We present some initial results from a pilot study using thissystem. Function Allocation for NextGen Airspace via Agents Nathan Schurrhas 2 papers, Paul Picciano, Janusz Mareckihas 3 papers


  Room clearing, in which building surveillance is conducted tosearch for criminals, continues to be a dangerous and difficultproblem in urban settings, for both the military as well as forpolice. In a typical setting, an unknown number of hostile forcesmay be located in a building, and they may be armed.Furthermore, there may be innocent civilians. The goal of thefriendly units is to enter the room and secure it, but without lossof life of friendly forces, hostile forces, and most especially ofinnocent civilians. It would be beneficial to allow robots to be apart of the friendly team, however it is very challenging to haverobots that do not either slow down or obstruct their humanteammate. This is especially difficult since nearly all robots in useby the military and police today are tele-operated. In this paper,we describe work we have developed in cooperation with thearmy, for the room clearing domain. We constructed an algorithmwhereby multiple agents, in the form of robots, can accomplish aroom clearing task. We augmented the agent algorithms tointroduce Adjustable Autonomy, allowing cooperation withhumans. We describe simulated results of the algorithm onbuilding maps, and furthermore we describe how we intend tonext conduct hardware tests, and eventual plans to field thesystem. This agent-based solution has great potential to increasethe acceptance and leverage of robotics in complex environments. Agent-based Coordination of Human-Multirobot Teams in Complex Environments Alan Carlin, Jeanine Ayers, Jeff Rousseau, Nathan Schurrhas 2 papers


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