Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours

The Fraunhofer Quantum Computing Portal - www.qc.fraunhofer.de - A web-based Simulator of Quantum Computing Processes

Fraunhofer FIRST develops a computing service and collaborative workspace providing a convenient tool for simulation and investigation of quantum algorithms. To broaden the twenty qubit limit of workstation-based simulations to the next qubit decade we provide a dedicated high memorized Linux cluster with fast Myrinet interconnection network together with a adapted parallel simulator engine. This simulation service supplemented by a collaborative workspace is usable everywhere via web interface and integrates both hardware and software as collaboration and investigation platform for the quantum community. The beta test version realizes all common one, two and three qubit gates, arbitrary one and two bit gates, orthogonal measurements as well as special gates like Oracle, Modulo function, Quantum Fourier Transformation and arbitrary Spin-Hamiltonians up to 31 qubits. For a restricted gate set it feasible to investigate circuits with up to sixty qubits. URL: http://www.qc.fraunhofer.d

Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio

Quantum Robots and Environments

Quantum robots and their interactions with environments of quantum systems are described and their study justified. A quantum robot is a mobile quantum system that includes a quantum computer and needed ancillary systems on board. Quantum robots carry out tasks whose goals include specified changes in the state of the environment or carrying out measurements on the environment. Each task is a sequence of alternating computation and action phases. Computation phase activities include determination of the action to be carried out in the next phase and possible recording of information on neighborhood environmental system states. Action phase activities include motion of the quantum robot and changes of neighborhood environment system states. Models of quantum robots and their interactions with environments are described using discrete space and time. To each task is associated a unitary step operator T that gives the single time step dynamics. T = T_{a}+T_{c} is a sum of action phase and computation phase step operators. Conditions that T_{a} and T_{c} should satisfy are given along with a description of the evolution as a sum over paths of completed phase input and output states. A simple example of a task carrying out a measurement on a very simple environment is analyzed. A decision tree for the task is presented and discussed in terms of sums over phase paths. One sees that no definite times or durations are associated with the phase steps in the tree and that the tree describes the successive phase steps in each path in the sum.Comment: 30 Latex pages, 3 Postscript figures, Minor mathematical corrections, accepted for publication, Phys Rev