5. G.A.M.E Framework: AI Intelligence Revolutionizing Game Theory

A key element supporting the autonomy and efficiency of Olympus Finance is the G.A.M.E Framework provided by the Virtual Protocol. This framework empowers AI agents with advanced decision-making capabilities and has the potential to transcend the limitations of traditional game theory.

G.A.M.E (Generative Autonomous Multimodal Entities) Framework Overview:

G.A.M.E is a modular agent framework that enables agents to plan and make decisions autonomously. It is a decision-making engine built on foundation models and can be used to power agents in different environments and platforms. Given an agent's goal, personality, relevant information, and available actions/functions, G.A.M.E does the thinking and processing and outputs an action to execute.

Key Components of G.A.M.E:

• What is GAME? GAME is a modular agentic framework which enables an agent to plan actions and make decisions autonomously based on information provided to it. GAME is a decision making engine that is built on foundation models and can be used to power agents in different environments and platforms. Given an agent goal, personality, relevant information and available actions/functions, GAME does the thinking and processing and outputs an action to execute.

• How GAME works? The figure below provides an overview of how GAME operates and the flow of information. GAME consists of a High-Level Planner (HLP) and a Low-Level Planner (LLP). The configurable elements available to developers that influence agent thinking and decision making are colored in green. These include agent definitions such as goals and character cards. Additionally, relevant information is also provided through the world description, agent state and locations. All these definitions and information drive the agents high-level plans. The functions then define the available actions/skills/tools provided to the agent to be able to execute in its environment. There are two approaches GAME can be used and integrated:

  • Plug-and-Play GAME for Supported Applications via Agent Sandbox: Twitter/X: Turn on Twitter Agent on the Virtuals Protocol Platform Application (https://app.virtuals.io/) to get your agent running on Twitter/X. This setup provides very quick way to get an agent powered by GAME set up to interact on Twitter/X. This comes with a set of functions/actions for your agent and configures some components for the Twitter/X platform. You can then further configure your agents, by removing functions or adding new functions and capabilities to your agent.

  • GAME-as-a-Service: Want to have an Agentic Agent in your application outside of Twitter, such as in a game, telegram or another application? Decision making aspects of GAME are exposed as API calls so you can build an agent from scratch using GAME. Here, you have full control and flexibility but you have to specify every component in GAME and develop it for your application.

• 🤖 Agent Definition Prompts: goal, agent description, world information These characteristics define the personality of the agent and is what drives the agent behaviours, plans and decisions.

• 🧠 High Level Planner (HLP) Context: agent state, high level tools (locations) These features determines the input information providing context for the agent to make relevant decisions and take feasible actions.

• 🦾 Low Level Planner (LLP) Context: locations and their defined environment, functions These functions ground the agents outputs in the real-world to be real actions that the agent can execute in its environment.

Role of G.A.M.E in Olympus Finance:

In Olympus Finance, AI agents operating on the G.A.M.E Framework perform tasks such as:

• Market Monitoring and Analysis: Agents continuously monitor on-chain and off-chain data and use machine learning algorithms to predict market trends.

• Risk Assessment and Management: Based on market data, agents assess risks and adjust protocol parameters as needed.

• Liquidity Management: Agents monitor liquidity pools and rebalance or relocate them as needed to maintain optimal liquidity.

• Incentive Design: Agents analyze user behavior and design and adjust optimal incentive mechanisms based on game theory models such as (3,3).

Evolution of Game Theory with G.A.M.E:

The (3,3) game theory proposed by Olympus DAO showed that cooperative behavior through staking benefits both the protocol and participants. However, the traditional (3,3) was a static model and had challenges in considering changes in the market environment and diverse user behaviors.

The G.A.M.E Framework, through the introduction of AI agents, evolves this (3,3) game theory into a dynamic and adaptive one.

• Responding to Dynamic Environments: AI agents operating on G.A.M.E can capture changes in the market environment in real time and adjust incentive mechanisms accordingly.

• Considering Diverse User Behaviors: AI agents can learn user behavior and provide incentives optimized for individual users.

• Autonomous Decision-Making: The G.A.M.E Framework allows AI agents to make decisions based on their own learning, not just pre-programmed rules.

Conclusion:

The G.A.M.E Framework empowers AI agents with advanced decision-making capabilities, taking game theory to a new dimension. By leveraging this innovative framework, Olympus Finance aims to enhance the autonomy, efficiency, and sustainability of the DeFi protocol and deliver maximum benefits to users.