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Integrating large language model (LLM) agents within game theory demonstrates their ability to replicate human-like behaviors through strategic decision making. In this paper, we introduce an augmented LLM agent, called the private agent, which engages in private deliberation and employs deception in repeated games. Utilizing the partially observable stochastic game (POSG) framework and incorporating in-context learning (ICL) and chain-of-thought (CoT) prompting, we investigated the private agent's proficiency in both competitive and cooperative scenarios. Our empirical analysis demonstrated that the private agent consistently achieved higher long-term payoffs than its baseline counterpart and performed similarly or better in various game settings. However, we also found inherent deficiencies of LLMs in certain algorithmic capabilities crucial for high-quality decision making in games. These findings highlight the potential for enhancing LLM agents' performance in multi-player games using information-theoretic approaches of deception and communication with complex environments. [ABSTRACT FROM AUTHOR]

Exponential advances in computational power have fueled advances in many disciplines, and biology is no exception. High-Performance Computing (HPC) is gaining traction as one of the essential tools in scientific research. Further advances to exascale capabilities will necessitate more energy-efficient hardware. In this article, we present our efforts to improve the efficiency of genome assembly on ARM-based HPC systems. We use vectorization to optimize the popular genome assembly pipeline of minimap2, miniasm, and Racon. We compare different implementations using the Scalable Vector Extension (SVE) instruction set architecture and evaluate their performance in different aspects. Additionally, we compare the performance of autovectorization to hand-tuned code with intrinsics. Lastly, we present the design of a CPU dispatcher included in the Racon consensus module that enables the automatic selection of the fastest instruction set supported by the utilized CPU. Our findings provide a promising direction for further optimization of genome assembly on ARM-based HPC systems. [ABSTRACT FROM AUTHOR]

We shall have a hard look at ethics and try to extract insights in the form of abstract properties that might become tools. We want to connect ethics to games, talk about the performance of ethics, introduce curiosity into the interplay between competing and coordinating in well-performing ethics, and offer a view of possible developments that could unify increasing aggregates of entities. All this is under a long shadow cast by computational complexity that is quite negative about games. This analysis is the first step toward finding modeling aspects that might be used in AI ethics for integrating modern AI systems into human society. [ABSTRACT FROM AUTHOR]

With the increasing complexity of power system structures and the increasing penetration of renewable energy, driven primarily by the need for decarbonization, power system operation and control become challenging. Changes are resulting in an enormous increase in system complexity, wherein the number of active control points in the grid is too high to be managed manually and provide an opportunity for the application of artificial intelligence technology in the power system. For power flow control, many studies have focused on using generation redispatching, load shedding, or demand side management flexibilities. This paper presents a novel reinforcement learning (RL)-based approach for the secure operation of power system via autonomous topology changes considering various constraints. The proposed agent learns from scratch to master power flow control purely from data. It can make autonomous topology changes according to current system conditions to support grid operators in making effective preventive control actions. The state-of-the-art RL algorithm—namely, dueling double deep Q-network with prioritized replay—is adopted to train effective agent for achieving the desired performance. The IEEE 14-bus system is selected to demonstrate the effectiveness and promising performance of the proposed agent controlling power network for up to a month with only nine actions affecting substation configuration. [ABSTRACT FROM AUTHOR]