Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.

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.

Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge -- \textit{situated inductive reasoning}, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles. In Mars, agents need to actively interact with their surroundings, derive useful rules and perform decision-making tasks in specific contexts. We conduct experiments on various RL-based and LLM-based methods, finding that they all struggle on this challenging situated inductive reasoning benchmark. Furthermore, we explore \textit{Induction from Reflection}, where we instruct agents to perform inductive reasoning from history trajectory. The superior performance underscores the importance of inductive reasoning in Mars. Through Mars, we aim to galvanize advancements in situated inductive reasoning and set the stage for developing the next generation of AI systems that can reason in an adaptive and context-sensitive way.

"In a world full of disruptions, the need for digital transformation is not a choice but a requirement that every organisation, big or small, has to undertake," says Vineet Jaiswal, chief digital and technology officer at Vedanta Resources Limited. With the advancements in IT, it becomes mandatory for all businesses to deploy technologies like analytics, AI and utilise cloud-based systems to help them keep abreast with modern ways that help businesses. In an exclusive interaction with Analytics India Magazine, Vineet Jaiswal speaks about how Vedanta has used the latest technologies to transform its business. AIM: What was the need for a mining company and conglomerate like Vedanta to undergo digital transformation? Vineet Jaiswal: According to me, there are four requirements or needs for digital transformation.

Detect Technologies announces a global agreement with Vedanta for deployment of T-Pulse, their internationally deployed AI-based workplace safety software. Vedanta Resources Limited is a globally diversified natural resources company and is among the top producers of major commodities, including zinc-lead-silver, iron ore, steel, copper, aluminium, oil and gas. The group engages more than 65,000 employees and contractors, primarily in India, Africa, Ireland and Australia. Managing EHS for such a diverse and spread-out organisation is a massive challenge. Driven by its commitment to GOAL ZERO, Vedanta started exploring AI–based solutions, which can infuse efficiency in this process.

New books are available for subscription. Deep Diamond() is a new Deep Learning library written in Clojure. Its goal is to be simple, superfast, and to support both CPU and GPU computing. But it's Clojure, you might say. Python is supported by Google and Facebook.

Diamonds are one of the most sought-after gems in the world. Commonly used for the production of aesthetic jewelry and in manufacturing thanks to its well-known physical properties of being one of the hardest naturally-occurring substances in existence, these precious stones come at a high price. A lack of a proper monitoring infrastructure throughout its production has led to dubious sourcing and ineffective mining and distribution, with a general lack of transparency throughout.

This Valentine's Day, many couples are planning engagements and consumers are buying diamond jewelry -- more so than at any other time of the year. But consumers are concerned that they are getting the best diamond in terms of quality when they commit to their partner. Sarnen, Switzerland-based online diamond advisory website The Diamond Pro surveyed a representative of 1,000 US adults to find out what concerned them about buying diamonds. The survey shows that overpaying, being taken advantage of and not getting a high-quality diamond are top concerns consumers face when shopping for a diamond. Consumers in general do not feel confident with key aspects of the diamond buying process.

You might think that digital technologies, often considered a product of "the West", would hasten the divergence of Eastern and Western philosophies. But within the study of Vedanta, an ancient Indian school of thought, I see the opposite effect at work. Thanks to our growing familiarity with computing, virtual reality and artificial intelligence, "modern" societies are now better placed than ever to grasp the insights of this tradition. Vedanta summarises the metaphysics of the Upanishads, a clutch of Sanskrit religious texts, likely written between 800 and 500 BCE. They form the basis for the many philosophical, spiritual and mystical traditions of the Indian sub-continent.

Would you ever consider purchasing diamond jewelry online? Lots of people have -- according to Research and Markets, the jewelry ecommerce market accounts for roughly 5 percent of the $257 billion overall jewelry industry, a share that's expected to triple by 2020. It's growing especially quickly in Asia, where the compound annual growth rate from 2011 to 2014 exceeded 60 percent. But if you're reluctant to dive in, you're not the only one -- there's a lot of uncertainty in the online diamond-buying market. High-resolution photos alone don't always tell the full story, particularly if they're lit unnaturally.