A Game Theoretic Analysis of the Convoy-ASW Problem

The problem of allocation of ASW forces assigned to an oceanic convoy in a submarine warfare environment is postulated as a two-person game with the payoff function being based on the "formula of random search". The opponents in the game are a convoy system and a submarine system. A submarine is given the option of attacking the convoy system either from afar with surface-launched missiles or near with torpedoes. The convoy system is defended by units capable of destroying submarines exercising either of their options. The optimal allocation of forces for both sides is shown to be a set of pure strategies which are dependent on the parameters of the model.http://www.archive.org/details/gametheoreticana00kilaLieutenant, United States NavyLieutenant, United States NavyApproved for public release; distribution is unlimited

Video game training in traumatic brain injury patients: an exploratory case report study using eye tracking

Remediation of attentional impairments is an essential component of cognitive rehabilitation after traumatic brain injury (TBI). Evidence from healthy participants has demonstrated attentional improvement following playing an action video game. This exploratory study investigated its application in TBI participants in a multiple baselines single case experimental design (SCED). Saccadic eye movements, recognized as the visible indicators of visual attention, were assessed to evaluate the effectiveness of the game training. Three severe TBI participants were trained in an action game for 10 hours. Saccadic eye movements during a self-paced saccade and an abstract visual search task were investigated during baseline, mid training and post-training. Using Percentage of Non-overlapping Data (PND), analysis showed consistent increase in the rate of the self-paced saccades in participants 1 (PND=80%) and 2 (PND=70%). In abstract search, fixation duration showed a minimally effective decrease for participant 2 (PND= 60%) and a moderately effective reduction in participant 3 (PND= 80%). Search time showed a highly effective reduction in participant 2 (PND = 100%) and moderately effective decrease in participant 3 (PND=70%). Overall, video game training might modify allocation of attention in eye movements. More evidence is required to validate the usefulness of this novel method of the cognitive training

A Reply to Mueller (2018) Supply Chain Collaboration: Further Insights into Incentive Alignment in the Beer Game Scenario

Purpose: We expand a previous discussion in this journal by proposing a new solution concept, based on game theory, for profit allocation with the aim of aligning incentives in collaborative supply chains. Design/methodology/approach: Through the Gately’s notion of propensity to disrupt, we minimize the desire of the nodes to leave the grand coalition in the search of a self-enforcing allocation mechanism. Findings: We discuss the benefits and limitations of this solution in comparison with more established alternatives (e.g. nucleolus and Shapley value). We show that it considers the bargaining power of the nodes, but it may not belong to the core. Originality/value: Finding a fair and self-enforcing scheme for incentive alignment, and specifically profit allocation, is essential to ensure the long-term sustainability of collaborative supply chains.Peer Reviewe

Toward multi-target self-organizing pursuit in a partially observable Markov game

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve intelligent agents' search and pursuit capabilities. We model a self-organizing system as a partially observable Markov game (POMG) with the features of decentralization, partial observation, and noncommunication. The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that distributed noncommunicating multi-agent coordination with partial observations in all three subtasks are effective, and 2048 FSC2 agents can perform efficient multi-target SOP with almost 100% capture rates