With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes during each evolutionary process using Large Language Models (LLMs) services. Experimental results show that our task-scheduling algorithm outperforms existing heuristic and traditional reinforcement learning methods. Additionally, we investigate the effects of different heuristic strategies and compare the evolutionary outcomes across various LLM services.

Pursuit-evasion is a multi-agent sequential decision problem wherein a group of agents known as pursuers coordinate their traversal of a spatial domain to locate an agent trying to evade them. Pursuit evasion problems arise in a number of import application domains including defense and route planning. Learning to optimally coordinate pursuer behaviors so as to minimize time to capture of the evader is challenging because of a large action space and sparse noisy state information; consequently, previous approaches have relied primarily on heuristics. We propose a variant of Thompson Sampling for pursuit-evasion that allows for the application of existing model-based planning algorithms. This approach is general in that it allows for an arbitrary number of pursuers, a general spatial domain, and the integration of auxiliary information provided by informants. In a suite of simulation experiments, Thompson Sampling for pursuit evasion significantly reduces time-to-capture relative to competing algorithms.

A common mechanism to assess trust in crowdworkers is to have them answer gold tasks. However, assigning gold tasks to all workers reduces the efficiency of the platform. We propose a mechanism that exploits transitivity so that a worker can be certified as trusted by other trusted workers who solve common tasks. Thus, trust can be derived from a smaller number of gold tasks assignment through multiple layers of peer relationship among the workers, a model we call deep trust. We use the derived trust to incentivize workers for high quality work and show that the resulting mechanism is dominant strategy incentive compatible. We also show that the mechanism satisfies a notion of fairness in that the trust assessment (and thus the reward) of a worker in the limit is independent of the quality of other workers.

Minesweeper is a famous single-player computer game, in which the grid of blocks contains some mines and the player is to uncover (probe) all blocks that do not contain any mines. Many heuristic strategies have been prompted to play the game, but the rate of success is not high. In this paper, we explore efficient strategies for the Minesweeper game. First, we show a counterintuitive result that probing the corner blocks could increase the rate of success. Then, we present a series of heuristic strategies, and the combination of them could lead to better results. We also transplant the optimal procedure on the basis of our proposed methods, and it achieves the highest rate of success. Through extensive simulations, a combination of heuristic strategies, "PSEQ", yields a success rate of 81.627(8)%, 78.122(8)%, and 39.616(5)% for beginner, intermediate, and expert levels respectively, outperforming the state-of-the-art strategies. Moreover, the developed quasi-optimal methods, combining the optimal procedure and our heuristic methods, raise the success rate to at least 81.79(2)%, 78.22(3)%, and 40.06(2)% respectively.

In online labor platforms such as Amazon Mechanical Turk, a good strategy to obtain quality answers is to take aggregate answers submitted by multiple workers, exploiting the wisdom of the crowd. However, human computation is susceptible to systematic biases which cannot be corrected by using multiple workers. We investigate a game-theoretic bonus scheme, called Peer Truth Serum (PTS), to overcome this problem. We report on the design and outcomes of a set of experiments to validate this scheme. Results show Peer Truth Serum can indeed correct the biases and increase the answer accuracy by up to 80%.

The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest a strategy that will allow nature-inspired algorithms to obtain results as good as those based on exhaustive search strategies; in order to do that, we first review, compare and improve current approaches to solving the puzzle; then we test one of these strategies with an estimation of distribution algorithm. Finally, we try to find a strategy that falls short of being exhaustive, and is then amenable for inclusion in nature inspired algorithms (such as evolutionary or particle swarm algorithms). This paper proves that by the incorporation of local entropy into the fitness function of the evolutionary algorithm it becomes a better player than a random one, and gives a rule of thumb on how to incorporate the best heuristic strategies to evolutionary algorithms without incurring in an excessive computational cost.