TruMet: An approach towards computing trust in multi-agent environment.

The growing popularity of multi-agent based approaches towards the formation and operation of virtual organizations (VO) present over the Internet, offer both opportunities and risks. One of the risks involved in such community is in the identification of trustworthy agent partners for transaction. In this paper we aim to describe our trust model which would contribute in measuring trust in the interacting agents. Named as TruMet, the trust metric model works on the basis of the parameters that we have identified as relevant to the features of the community. The model primarily analyses trust value on the basis of the agent¿s reputation, as provided by the agent itself, and the agent¿s aggregate rating as provided by the witness agents. The final computation of the trust value is given by a weighted average of these two components. While computing the aggregate rating, a weight based method has been adopted to discount the contribution of possibly un-fair ratings by the witness agents

The 2013 Multi-objective Physical Travelling Salesman Problem Competition

This paper presents the game, framework, rules and results of the Multi-objective Physical Travelling Salesman Problem (MO-PTSP) Competition, that was held at the 2013 IEEE Conference on Computational Intelligence in Games (CIG). The MO-PTSP is a real-time game that can be seen as a modification of the Travelling Salesman Problem, where the player controls a ship that must visit a series of waypoints in a maze while minimizing three opposing goals: Time spent, fuel consumed and damage taken. The rankings of the competition are computed using multi-objective concepts, a novel approach in the field of game artificial intelligence competitions. The winning entry of the contest is also explained in detail. This controller is based on the Monte Carlo Tree Search algorithm, and employed Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for parameter tuning

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions

This paper presents a number of approaches for solving a real-time game consisting of a ship that must visit a number of waypoints scattered around a 2-D maze full of obstacles. The game, the Physical Traveling Salesman Problem (PTSP), which featured in two IEEE conference competitions during 2012, provides a good balance between long-term planning (finding the optimal sequence of waypoints to visit), and short-term planning (driving the ship in the maze). This paper focuses on the algorithm that won both PTSP competitions: it takes advantage of the physics of the game to calculate the optimal order of waypoints, and it employs Monte Carlo tree search (MCTS) to drive the ship. The algorithm uses repetitions of actions (macro actions) to reduce the search space for navigation. Variations of this algorithm are presented and analyzed, in order to understand the strength of each one of its constituents and to comprehend what makes such an approach the best controller found so far for the PTSP. © 2009-2012 IEEE

A psychometric evaluation of emotional responses to horror music

This research explores and designs an effective experimental interface to evaluate people's emotional responses to horror music. We studied methodological approaches by using traditional psychometric techniques to measure emotional responses, including self-reporting, and galvanic skin response (GSR). GSR correlates with psychological arousal. It can help circumvent a problem in self-reporting where people are unwilling to report particular felt responses, or confuse perceived and felt responses. We also consider the influence of familiarity. Familiarity can induce learned emotional responses rather than listeners describing how it actually makes them feel. The research revealed different findings in self-reports and GSR data. Both measurements had an interaction between music and familiarity but show inconsistent results from the perspective of simple effects

AI and Automatic Music Generation for Mindfulness

This paper presents an architecture for the creation of emotionally congruent music using machine learning aided sound synthesis. Our system can generate a small corpus of music using Hidden Markov Models; we can label the pieces with emotional tags using data elicited from questionnaires. This produces a corpus of labelled music underpinned by perceptual evaluations. We then analyse participant’s galvanic skin response (GSR) while listening to our generated music pieces and the emotions they describe in a questionnaire conducted after listening. These analyses reveal that there is a direct correlation between the calmness/scariness of a musical piece, the users’ GSR reading and the emotions they describe feeling. From these, we will be able to estimate an emotional state using biofeedback as a control signal for a machine-learning algorithm, which generates new musical structures according to a perceptually informed musical feature similarity model. Our case study suggests various applications including in gaming, automated soundtrack generation, and mindfulness

Efficiency improvements in a dichroic dye-doped liquid crystal Fresnel lens

A dichroic dye-doped liquid crystal Fresnel lens was fabricated and investigated to observe the combination of phase and amplitude modulation based focusing. An anthraquinone dichroic dye was doped into a liquid crystal host, which when in the Fresnel lens configuration generates a Fresnel zone plate with alternating “transparent” and “opaque” zones. The zones were induced by using photo-alignment of a light-sensitive alignment layer to generate the alternating pattern. The voltage dependency of efficiency for the dye doped and pure liquid crystal Fresnel devices were investigated. Incorporation of dyes into the device yielded a significant 4 % improvement in relative efficiency in the lens, giving a maximum of 37 % achieved in the device, much closer to the theoretical 41 % limit when compared with the non dye doped device. The input polarization dependence of efficiency was also investigated, showing very small fluctuations (±1.5 %), allowing further insight into the effect of fabrication method on these liquid crystal Fresnel devices