Developing intelligent agents for long-term cooperation in dynamic open-world scenarios is a major challenge in multi-agent systems. Traditional Multi-agent Reinforcement Learning (MARL) frameworks like centralized training decentralized execution (CTDE) struggle with scalability and flexibility. They require centralized long-term planning, which is difficult without custom reward functions, and face challenges in processing multi-modal data. CTDE approaches also assume fixed cooperation strategies, making them impractical in dynamic environments where agents need to adapt and plan independently. To address decentralized multi-agent cooperation, we propose Decentralized Adaptive Knowledge Graph Memory and Structured Communication System (DAMCS) in a novel Multi-agent Crafter environment. Our generative agents, powered by Large Language Models (LLMs), are more scalable than traditional MARL agents by leveraging external knowledge and language for long-term planning and reasoning. Instead of fully sharing information from all past experiences, DAMCS introduces a multi-modal memory system organized as a hierarchical knowledge graph and a structured communication protocol to optimize agent cooperation. This allows agents to reason from past interactions and share relevant information efficiently. Experiments on novel multi-agent open-world tasks show that DAMCS outperforms both MARL and LLM baselines in task efficiency and collaboration. Compared to single-agent scenarios, the two-agent scenario achieves the same goal with 63% fewer steps, and the six-agent scenario with 74% fewer steps, highlighting the importance of adaptive memory and structured communication in achieving long-term goals. We publicly release our project at: https://happyeureka.github.io/damcs.

This paper outlines the ideas behind developing a graph-based heuristic-driven routing algorithm designed for a particular instance of a goods transportation problem with a single good type. The proposed algorithm solves the optimization problem of satisfying the demand of goods on a given undirected transportation graph with minimizing the estimated cost for each traversed segment of the delivery path. The operation of the routing algorithm is discussed and overall evaluation of the proposed problem solving technique is given. HE transportation problem is one of the well-known and hot topics both in mathematics and economics. It was first conceptualized by the French mathematician Gaspard Monge back in 1781 [1].

In July 1950, a small group of American soldiers called Task Force Smith were all that stood in the way of an advance of North Korean armor. The soldiers' only anti-armor weapons were bazookas left over from World War II. The soldiers of Task Force Smith quickly found themselves firing round after round of bazooka ammunition into advancing North Korean T-34s only to see them explode harmlessly on the heavily armored tanks. Within seven hours, 40 percent of Task Force Smith were killed or wounded, and the North Korean advance rolled on.1 The shortcomings of the bazooka were no surprise. However, budget cutbacks after World War II scuttled adoption of an improved design.

Artificial Intelligence (AI) is nowadays the most trending topic in IT and data science. A significant number of data scientists focus on the development of AI systems such as Machine Learning and Natural Language Processing. At the same time, there is huge demand for AI systems and applications in almost every sector of the economy, including for example finance, manufacturing, defense, security and healthcare. In this context, there is however much confusion between true AI systems and systems that claim to support AI as a marketing buzzword. In simple terms, AI is defined as the capability of machines to imitate intelligent human behavior and solving problems much in the same way humans do.

The " Artificial Intelligence-based Cybersecurity Market " 2019-2022 research report provides a detailed overview of industry. It covers the growth aspects of industry. Artificial Intelligence-based Cybersecurity market report includes key strategies and the effect of key players in the Artificial Intelligence-based Cybersecurity market. Additionally, it provides the market revenue, share, SWOT analysis, growth factors of company as well as manufacturers in the market.

Objective To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).