On behavior strategy solutions in finite extended decision processes

Techniques for finding best behavior strategies on arbitrary information collection scheme

On behavior strategy solutions of finite two- person constant-sum extended games

Recall-sensitivity and behavior strategy solutions in finite two-person constant-sum extended game

Star-forming cores embedded in a massive cold clump: Fragmentation, collapse and energetic outflows

The fate of massive cold clumps, their internal structure and collapse need to be characterised to understand the initial conditions for the formation of high-mass stars, stellar systems, and the origin of associations and clusters. We explore the onset of star formation in the 75 M_sun SMM1 clump in the region ISOSS J18364-0221 using infrared and (sub-)millimetre observations including interferometry. This contracting clump has fragmented into two compact cores SMM1 North and South of 0.05 pc radius, having masses of 15 and 10 M_sun, and luminosities of 20 and 180 L_sun. SMM1 South harbours a source traced at 24 and 70um, drives an energetic molecular outflow, and appears supersonically turbulent at the core centre. SMM1 North has no infrared counterparts and shows lower levels of turbulence, but also drives an outflow. Both outflows appear collimated and parsec-scale near-infrared features probably trace the outflow-powering jets. We derived mass outflow rates of at least 4E-5 M_sun/yr and outflow timescales of less than 1E4 yr. Our HCN(1-0) modelling for SMM1 South yielded an infall velocity of 0.14 km/s and an estimated mass infall rate of 3E-5 M_sun/yr. Both cores may harbour seeds of intermediate- or high-mass stars. We compare the derived core properties with recent simulations of massive core collapse. They are consistent with the very early stages dominated by accretion luminosity.Comment: Accepted for publication in ApJ, 14 pages, 7 figure

What a Neural Network Can Learn about Othello

Conventional Othello programs are based on a thorough analysis of the game, and typically employ sophisticated evaluation functions and supervised learning techniques that use large expert-labeled game databases. This paper presents an alternative method that trains a neural network to evaluate Othello positions via temporal difference (TD) learning. The approach is based on a network architecture that reflects the spatial and temporal organization of the problem domain. The network begins with random weights, and through self-play achieves an intermediate level of play. We also present a simple and effective method for analyzing what the network learned. 1 Introduction The game of Othello is a descendant of an old Japanese board game. Like chess, it is a deterministic, perfect information, zero-sum game of strategy between two players. The limited length of the game, typically sixty moves, and the small average branching factor, approximately seven, give Othello a complexity greater..