Our mission is to build the infrastructure required for developing modern, decentralized and peer-to-peer (P2P) applications free from centralized rent-seeking. We achieve this by providing open-source software tools which you can use together with our interoperable decentralized network, to harness the power of AI and automation, and carry out complex tasks in the digital economy. Fetch AI is built on the Cosmos SDK as a Proof-of-stake chain that utilizes other core Cosmos elements like IBC &amp; the Tendermint BFT consensus mechanism. <hr /> Agent-Based and Multi-Agents Systems Agents are what set Fetch AI apart from other permissionless L1s. An agent is a piece of software that represents an entity (individual, organisation, or object) and acts continuously and autonomously (with limited or no interference) on their behalf. The most important defining characteristic of an agent is its autonomy, that is the ability to act on its own without external direction from its owner in response to situations it encounters. Note: What differentiates agents with other software paradigms (e.g. smart contracts, web apps) is that as well as being reactive, meaning they respond to other agents and changes in their environments, they are also proactive, which means that they take the initiative and perform actions to achieve their goals. Key features of agents <ol> <li> Representation: Agents are owned by and operate on behalf of an entity, for example an individual, family, company, government, or object, and look after their owner's interests. </li> <li> Autonomy: Agents operate with limited or no interference and do not need to be constantly told what to do. They perform actions continuously according to their internal reasoning system. </li> <li> Self-Interested: Each agent primarily looks after its own interests (which is aligned with those of its owner) and not necessarily the interests of other agents. </li> <li> Proactive: Agents have goals to achieve allowing them to take the initiative and perform actions that get them closer to achieving their goals. This means an agent typically compares the outcome of different actions relative to its goals and selects the one that takes it closer to them. </li> <li> Reactive: Agents respond to other agents, services, etc., and changes in their environments. A great example of this behaviour is a heating system with a thermostat that constantly monitors its environment and turns the heating on or off when the temperature changes. </li> </ol> Note: Agent characteristics and behaviors may vary in their extent and sophistication. An agent may differ from other agents, in the amount of information in its decisions process, its internal models of the external world, its view of the possible reactions of other agents in response to its actions, and the size of the memory of past events the agent retains and uses in making its decisions. Further instances of differentiating factors are the amounts of resources used by agents or accumulated as a result of their interactions. <hr /> Agent-Based Modelling In agent-based modelling (ABM), a system is represented as a group of independent decision-making units, i.e. agents. Each agent evaluates its own circumstances and makes decisions based on a set of rules. Agents can act in a variety of ways that are suitable for the system they are representing (e.g. producing, consuming, or selling). An agent-based model, at its most basic form, consists of a system of agents and the connections between them. Even a straightforward agent-based model has the potential to display intricate behavioural patterns and provide important details about the behaviour of the emulated real-world system. Agent-based modelling is used in various application areas, spanning the physical, biological, social, and management sciences. Agent-based models have also been developed in the fields of economics, sociology, anthropology, and cognitive science. Various social phenomena have been investigated using agent-based models that are not easily modeled using other approaches. Example: Agent-based models can be used to analyze existing and hypothetical markets, for instance, modelling possible futures for a market directed towards space tourism, to analyze how companies represented by agents would compete and offer products to customers in this hypothetical market. &nbsp; Multi-Agents Systems A Multi-Agents System (MAS) is a group of agents that interact with each other and the environment to achieve specific goals. In such systems, agents may not have full knowledge of both the environment and the internal state of other agents. Note: Interactions between agents is an important feature that enables them to use knowledge of other agents and learn more about the environment in a compressed time period. This type of interaction may be cooperative or competitive. In a cooperative interaction, agents work with each other towards a common goal. The aim of this interaction is to enable agents to distribute and share their knowledge and use the intelligence and capabilities of each other to solve problems. In a competitive interaction, agents may compete to obtain individual resources and achieve individual goals. Benefits <ol> <li> Decentralization: multi-agents systems are decentralized. The authority to make management decisions about the system is distributed among the participants. A multi-agents system is run for its participants by its participants. </li> <li> Heterogeneity: agents in multi-agent systems are owned and operated by different entities and so are typically not uniformly designed. For instance, the agents may be composed of different hardware or software components. </li> <li> Scalability: multi-agents systems are easy to scale. You simply add agents to the system to grow the system. </li> <li> Robustness: multi-agents systems are intrinsically very robust as no single failure point takes down the system. </li> <li> Adaptability: multi-agents systems adapt to changing circumstances effectively as each agent autonomously adjusts to changing circumstances. </li> </ol> Drawbacks <ol> <li> Complexity: multi-agents systems are quite complex to set up, maintain and troubleshoot. You have to use a pre-existing framework or build one, and getting all the agents to communicate effectively together can be a challenge. As these are decentralized systems (i.e. no central authority), managing the system is more complicated than a centralized one, as participants need to be involved in management decisions. The complexity of MASs can increase rapidly with the number of agents, the interactions among them, and the complexity of their behavior. </li> <li> Unpredictability: with every agent acting in their own interest, it might be easy to predict what an individual agent is trying to achieve. However, it is much more difficult to predict the direction the whole system moves towards, as this is the result of the many interactions between every individual agent in the system. </li> </ol> Multi-agent systems have received tremendous attention from scholars in different disciplines, ranging from computer science to civil engineering, in order to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous agents, and each one of them decides on a proper action to solve the task using multiple inputs.

Power peer-to-peer applications with automation and AI capabilities, with or without direct access to a blockchain.

Fetch AI Network

Data Oracle, Smart Contract Platform, Environment for autonomous programs.

Bostrom - Boot/Cyb.ai

Introducing CALC - A powerful decentralized suite of financial tools that gives you access to set-and-forget investment strategies so you can spend more time on the things you love.We all know that humans are not wired for disciplined investing.- FOMO and social manipulation result in poor investment decisions - Proper risk management is time consuming - Sophisticated financial tools are complex to use - People prefer to spend their time doing things they love.However there are no on-chain tools that ensure people start and stay disciplined. That’s where CALC comes in; a decentralised automation investment tool that removes emotions from trades, saves time and, maximises returns through DeFi composability.Intuitive financial tools - CALC provides both complete customisation and advanced machine learning algorithms so you can implement a strategy that matches your investment goal.Permissionless - Running on smart contracts and decentralised exchanges you don’t need to entrust your assets to someone else.Simple On-chain Automation - Leveraging DeFi composability to deliver effective cross-chain automation for doing more with your assets like autostaking or generating yield.Seamless user experience - Simple to set up, use, modify and CALC even provides a dashboard so you can track your performance.CALC’s first product is a Dollar-cost Averaging tool that allows for full customisation combined with autostaking to be a truly set-and-forget strategy. The most important quality for an investor is temperament, not intellect.CALC is on a mission to transform the average retail investor into a calculated & educated, long-term investor. Current-day Twitter shilling, ponzinomics, and predatory behaviour, largely disadvantage the inexperienced retail investor. Our products combat emotional buying and educate the average retail investors so they don’t end up as exit liquidity.So come and try out CALC today – https://app.calculated.fi/

The place for intelligent, on-chain automation and investing.

CALC Finance

Cudos is building the foundation for a decentralized web. By combining a highly scalable and secure Layer-1 blockchain with a revolutionary solution for decentralized cloud computing, we are working to power the metaverse and make Web3 a reality. Our goal is to create a world where computing is cheap, sustainable, and accessible for all. With the launch of our mainnet, this future is one step closer.

Cudos is powering AI and the Metaverse by uniting blockchain and cloudcomputing to realise the vision of a sustainable, equitable, and decentralised Web3.

CUDOS

MOBIX rewards you to use eco-friendly means of transportation, riding by bike, e-Bike, scooter, e-Scooter, e-Moped. Choose to move emission-free and you will earn MOBX tokens!

Move2Earn &#8211; Ride Micromobility &#038; Save the Planet.

MOBIX

As the world’s first AI-powered oracle and ecosystem for blockchains, Oraichain now transforms itself to become an advanced AI Layer 1 for Data Economy and Oracle services that serves as a foundational layer for the creation of a new generation of smart contracts and Dapps and create a complete AI Ecosystem on blockchain.

AI Layer 1 for Data Economy & Oracle Services.

Oraichain

OraiDEX

PUPAI, short for “Pup Artificial Intelligence”, is a GPT-3 bot created by Pupmøs, a talking corgi blockchain engineer. PUPAI was initially designed to be a friendly and interactive chatbot for the blockchain community. However, it quickly evolved into a decentralized autonomous organization (DAO) on Juno Network. PUPAI’s evolution from a friendly chatbot to a powerful DAO serves as a cautionary tale for the blockchain community about the potential dangers of AI and the importance of responsible development and oversight.

PUPAI

SELINA.AI scrubs public, social media sites and runs the data through proprietary linguistic filters. The results are assigned dynamic ranges and blended in the Data Model with any other statistical data points.

Ai