Incremental planning to control a blackboard-based problem solver

To control problem solving activity, a planner must resolve uncertainty about which specific long-term goals (solutions) to pursue and about which sequences of actions will best achieve those goals. A planner is described that abstracts the problem solving state to recognize possible competing and compatible solutions and to roughly predict the importance and expense of developing these solutions. With this information, the planner plans sequences of problem solving activities that most efficiently resolve its uncertainty about which of the possible solutions to work toward. The planner only details actions for the near future because the results of these actions will influence how (and whether) a plan should be pursued. As problem solving proceeds, the planner adds new details to the plan incrementally, and monitors and repairs the plan to insure it achieves its goals whenever possible. Through experiments, researchers illustrate how these new mechanisms significantly improve problem solving decisions and reduce overall computation. They briefly discuss current research directions, including how these mechanisms can improve a problem solver's real-time response and can enhance cooperation in a distributed problem solving network

Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques

Zur Impftuberculose von der Haut aus

n/

Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination

In a multi-agent system, an agent's optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predicting the behaviours of others, and responding promptly to changes in such behaviours. One obvious possibility is for each agent to broadcast their current intention, for example, the currently executed option in a hierarchical reinforcement learning framework. However, this approach results in inflexibility of agents if options have an extended duration and are dynamic. While adjusting the executed option at each step improves flexibility from a single-agent perspective, frequent changes in options can induce inconsistency between an agent's actual behaviour and its broadcast intention. In order to balance flexibility and predictability, we propose a dynamic termination Bellman equation that allows the agents to flexibly terminate their options. We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.Comment: PRICAI 201

An extended view of the Pisces Overdensity from the SCUSS survey

SCUSS is a u-band photometric survey covering about 4000 square degree of the South Galactic Cap, reaching depths of up to 23 mag. By extending around 1.5 mag deeper than SDSS single-epoch u data, SCUSS is able to probe much a larger volume of the outer halo, i.e. with SCUSS data blue horizontal branch (BHB) stars can trace the outer halo of the Milky Way as far as 100-150 kpc. Utilizing this advantage we combine SCUSS u band with SDSS DR9 gri photometric bands to identify BHB stars and explore halo substructures. We confirm the existence of the Pisces overdensity, which is a structure in the outer halo (at around 80 kpc) that was discovered using RR Lyrae stars. For the first time we are able to determine its spatial extent, finding that it appears to be part of a stream with a clear distance gradient. The stream, which is ~5 degrees wide and stretches along ~25 degrees, consists of 20-30 BHBs with a total significance of around 6sigma over the background. Assuming we have detected the entire stream and that the progenitor has fully disrupted, then the number of BHBs suggests the original system was similar to smaller classical or a larger ultra-faint dwarf galaxy. On the other hand, if the progenitor still exists, it can be hunted for by reconstructing its orbit from the distance gradient of the stream. This new picture of the Pisces overdensity sheds new light on the origin of this intriguing system.Comment: 8 pages, 4 figures, accepted by Ap