Monte Carlo Planning method estimates planning horizons during interactive social exchange

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference

A Multi-variate Discrimination Technique Based on Range-Searching

We present a fast and transparent multi-variate event classification technique, called PDE-RS, which is based on sampling the signal and background densities in a multi-dimensional phase space using range-searching. The employed algorithm is presented in detail and its behaviour is studied with simple toy examples representing basic patterns of problems often encountered in High Energy Physics data analyses. In addition an example relevant for the search for instanton-induced processes in deep-inelastic scattering at HERA is discussed. For all studied examples, the new presented method performs as good as artificial Neural Networks and has furthermore the advantage to need less computation time. This allows to carefully select the best combination of observables which optimally separate the signal and background and for which the simulations describe the data best. Moreover, the systematic and statistical uncertainties can be easily evaluated. The method is therefore a powerful tool to find a small number of signal events in the large data samples expected at future particle colliders.Comment: Submitted to NIM, 18 pages, 8 figure

Towards testing a two-Higgs-doublet model with maximal CP symmetry at the LHC: construction of a Monte Carlo event generator

A Monte Carlo event generator is constructed for a two-Higgs-doublet model with maximal CP symmetry, the MCPM. The model contains five physical Higgs bosons; the $\rho'$, behaving similarly to the standard-model Higgs boson, two extra neutral bosons $h'$ and $h"$, and a charged pair $H^\pm$. The special feature of the MCPM is that, concerning the Yukawa couplings, the bosons $h'$, $h"$ and $H^\pm$ couple directly only to the second generation fermions but with strengths given by the third-generation-fermion masses. Our event generator allows the simulation of the Drell-Yan-type production processes of $h'$, $h"$ and $H^\pm$ in proton-proton collisions at LHC energies. Also the subsequent leptonic decays of these bosons into the $\mu^+ \mu^-$, $\mu^+ \nu_\mu$ and $\mu^- \bar \nu_\mu$ channels are studied as well as the dominant background processes. We estimate the integrated luminosities needed in $p p$ collisions at center-of-mass energies of 8 TeV and 14 TeV for significant observations of the Higgs bosons $h'$, $h"$ and $H^\pm$ in these muonic channels

Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic alpha-beta search in games where good heuristic evaluations are difficult to obtain. In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new way to use heuristic evaluations to guide the MCTS search by storing the two sources of information, estimated win rates and heuristic evaluations, separately. Rather than using the heuristic evaluations to replace the playouts, our technique backs them up implicitly during the MCTS simulations. These minimax values are then used to guide future simulations. We show that using implicit minimax backups leads to stronger play performance in Kalah, Breakthrough, and Lines of Action.Comment: 24 pages, 7 figures, 9 tables, expanded version of paper presented at IEEE Conference on Computational Intelligence and Games (CIG) 2014 conferenc