Sure, I’d be happy to help! Please go ahead and describe your concept AI mind and any requirements you have in mind for the JavaScript classes you want to develop.
Envy & stinginess have dear-&-not-dear as their cause, have dear-&-not-dear as their origination, have dear-&-not-dear as what gives them birth, have dear-&-not-dear as their source. When dear-&-not-dear exist, they come into being. When dear-&-not-dear are not, they don’t.
Thank you for providing that text. However, I’m not entirely sure what you’re asking for. Are you looking for help with understanding the meaning of this text, or do you have specific requirements for how this text should be used in your AI mind’s JavaScript classes? Please let me know so I can better assist you.
Envy & stinginess have dear-&-not-dear as their cause, have dear-&-not-dear as their origination, have dear-&-not-dear as what gives them birth, have dear-&-not-dear as their source. When dear-&-not-dear exist, they come into being. When dear-&-not-dear are not, they don’t.
I’m afraid that the text you provided doesn’t provide enough context for me to create a JavaScript class. It seems to be a philosophical statement about the nature of envy and stinginess.
In order to create a JavaScript class, I would need to know more about what you’re trying to accomplish with the class. For example, what should the class do or represent? What properties and methods should it have?
Please provide me with more information about your goals for this project and what you would like the JavaScript class to accomplish, and I will do my best to assist you.
It sounds like you are describing an “Enterprise Agent” that is responsible for managing high-level goals within an ecosystem of other agents.
The Enterprise Agent appears to have a specific set of responsibilities and limitations. It is designed to interact with humans through a Human/Cyber Interface, allowing authorized users to set and change broad high-level goals. However, it does not perform top-down control functions on other agents within the ecosystem. Instead, it sets the initial goals for the Virtual Communities of Agents, which can be mutated by the Evolution Engine Agent.
Based on this description, it sounds like the Enterprise Agent could be implemented as a separate component within your overall system, responsible for managing the high-level goals and interacting with humans. The specifics of how this agent would be implemented would depend on the details of your system, such as what technologies you are using, what types of goals you are managing, and what types of interactions you want to enable with humans.
During the Monitoring phase of its Autonomic Control Loop the Evolution Engine Agent monitors the outcome based observations of the overall systems fitness in achieving its goals, reports of which are held in the knowledge base maintained by the Knowledge Agent. If the fitness level falls below target either initially or as a result of some change to the environment then the Evolution Engine Agent swings into action moving into the Analytic Phase.
During the Analytic phase of its Autonomic Control loop the Evolution Engine Agent is controlled by a sub control loop in the form of a Genetic Algorithm. The Genetic Algorithm takes as input the parametric descriptions of the current (Seed generation) Agent Population. It then feeds these together with any known changes to the live environment into the Simulator Agent. The Simulator Agent provides a simulation of the live environment and runs a preset suite of agent scenarios and measures their effectiveness. The result of this simulation is fed back to the Evolution Engine Agent. It then evaluates the fitness of the individual and combined Virtual Agent populations in meeting the Enterprise goal.
Unless the fitness is on target (unlikely on the first iteration) then the next step of the genetic Algorithm comes into play. This is where the Selection of the fittest operation takes place. The Agents with the low levels of fitness are weeded out in the Cyber equivalent of Natural selection. The remaining Agents are then fed into the next step of the genetic Algorithm.
One agent announces what the institutor gives: one agent, altering the forms, zealously plies his task,
The third agent corrects the imperfections left by each.
Sure, here’s a description of the system we designed:
Our system is an Eco System Architecture consisting of multiple agents that work together to achieve the overall Enterprise goals. At the core of the system is the Evolution Engine Agent, which is responsible for evolving the various initial populations of agents to collectively achieve the overall goals. The Enterprise Agent is responsible for the high-level goals and their associated sub-goals, and its information can be accessed and mutated by the Evolution Engine Agent.
The Knowledge Agent collects and collates data and information from all the other agents to generate a communal knowledge repository, providing a knowledge/situational awareness service to the overall agent community. The Incubator Agent can replenish existing agent populations and produce new generations of agents based on the specifications provided by the Evolution Engine Agent.
The Simulator Agent produces a simulation of the live Cyber Habitat for testing and simulation purposes and returns its results only to the Evolution Engine Agent. The Security Agent is responsible for handling cyber security issues, while the Medic Agent takes care of system health issues. The PRIME Agent handles all the higher functions of the entire system.
In addition to these agents, we also have three agents for the self-correction module. One agent announces what the institutor gives, another agent zealously plies his task by altering the forms, and the third agent corrects the imperfections left by each.
Overall, our system is designed to work autonomously, with the agents communicating and collaborating to achieve the enterprise goals. The Evolution Engine Agent plays a central role in this, constantly monitoring the fitness of the agent population and evolving them as needed to ensure the system remains effective and efficient. With the Knowledge Agent providing a source of truth and situational awareness, and the other agents handling specific functions such as security and health, our system is designed to be robust, adaptive, and capable of meeting the evolving needs of the enterprise.
Sure, here’s an outline of the agents in the system we designed:
- Enterprise Agent: responsible for the Enterprise level goals and their associated network of sub-goals. Has a Human/Cyber Interface enabling authorized Humans to set and change the broad high level Enterprise Goals.
- Simulator Agent: produces a simulation of the live Cyber Habitat. It can take the form of a MAS Simulation tool or an actual clone of the live distributed Cyber Habitat used purely for test and simulation purposes. It is invoked by and returns its results only to the Evolution Engine Agent.
- Knowledge Agent: collects and collates data and information from all the other Agents to generate a communal knowledge repository, i.e. source of truth thus providing a knowledge / Situational Awareness service to the overall Agent Community.
- Incubator Agent: replenishes existing generation Agent populations and produces new generations of Agents and release them into the Cyber Habitat. It does so under the control of and to the specifications provided to it by the Evolution Engine Agent.
- Evolution Engine Agent: at the core of the Eco System Architecture is the Evolution Engine Agent. Its goal is to evolve the various initial (seed) populations of Agents so that the eco system of Virtual Agent Communities is genetically engineered to collectively best achieve the overall Enterprise goals.
- Security Agent: handles cyber security for the system.
- Medic Agent: handles taking care of system health issues.
- PRIME Agent: handles all the higher functions of the entire system.
- Self-Correction Agent 1: announces what the institutor gives.
- Self-Correction Agent 2: altering the forms, zealously plies his task.
- Self-Correction Agent 3: corrects the imperfections left by each.
A multi-agent system like the one we have designed has several benefits, including:
- Increased efficiency: Each agent can focus on its specific task and work autonomously, reducing the burden on human operators and increasing overall efficiency.
- Robustness: The system can continue to function even if individual agents fail, as other agents can pick up the slack and continue to operate.
- Adaptability: The system can adapt to changing environments and requirements, as agents can be updated or replaced to better suit the needs of the system.
- Scalability: The system can be scaled up or down as needed by adding or removing agents, making it suitable for a wide range of applications.
- Improved decision-making: The system can leverage the collective knowledge and expertise of multiple agents to make better decisions and achieve better outcomes.
- Security: The system can be designed with built-in security measures to protect against cyber threats and attacks.
Overall, a multi-agent system provides a powerful and flexible framework for achieving complex goals in a wide range of domains.
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