AI Agent-based Software System

An AI Agent-based Software System is a fully-automated software-based system that consists of interacting software agents.

• Context:
  • Task Input: Contract Documents, Organization Requirements, Process Configurations
    • Optional Input: Templates, Workflow Rules, Compliance Settings
  • Task Output: Contract Analysis Results, Process Status Updates, Compliance Reports
  • Task Performance Measure: Contract System Metrics such as processing efficiency, compliance accuracy, and user satisfaction
  • ...
  • It can (typically) support Agent-based Interaction through agent communication protocols.
  • It can (typically) maintain Agent-based System Dynamics via agent behaviors.
  • It can (typically) achieve Agent-based System Goals through collective actions.
  • It can (typically) implement Agent-based Contract Lifecycle Management through workflow engines.
  • It can (typically) enable Agent-based Contract Creation through template systems.
  • It can (typically) support Agent-based Contract Analysis through review engines.
  • It can (typically) facilitate Agent-based Contract Approval through routing systems.
  • It can (typically) manage Agent-based Contract Storage through repository systems.
  • It can (often) enable Agent-based Collaboration via shared environments.
  • It can (often) manage Agent-based Resources across system components.
  • It can (often) coordinate Agent-based Activity through system protocols.
  • It can (often) implement Agent-based Learning Mechanisms.
  • It can (often) generate Agent-based Contract Reports through analytics engines.
  • It can (often) track Agent-based Obligation Status through monitoring systems.
  • It can (often) enable Agent-based Team Access through permission systems.
  • It can (often) maintain Agent-based Audit Trails through logging systems.
  • It can (often) support Agent-based Version Control through content systems.
  • ...
  • It can range from being a Simple Agent-based System to being a Complex Agent-based System, depending on its system complexity.
  • It can range from being a Tightly-coupled Agent-based System to being a Loosely-coupled Agent-based System, depending on its agent coupling.
  • It can range from being a Centralized Agent-based System to being a Distributed Agent-based System, depending on its system architecture.
  • It can range from being a Homogeneous Agent-based System to being a Heterogeneous Agent-based System, depending on its agent diversity.
  • It can range from being a Static Agent-based System to being a Dynamic Agent-based System, depending on its system adaptability.
  • It can range from being a Reactive Agent-based System to being a Proactive Agent-based System, depending on its agent behavior pattern.
  • It can range from being a Single-Goal Agent-based System to being a Multi-Goal Agent-based System, depending on its goal complexity.
  • It can range from being a Limited-Environment Agent-based System to being an Open-Environment Agent-based System, depending on its environmental scope.
  • It can range from being a Single-Learning Agent-based System to being a Collective Learning Agent-based System, depending on its learning scope.
  • It can range from being a Supervised Learning Agent-based System to being a Self-Learning Agent-based System, based on its learning autonomy.
  • It can range from being an Agent-based Content-Centric System to being an Agent-based Knowledge-Centric System, depending on its data approach.
  • It can range from being an Agent-based Standalone System to being an Agent-based Integrated System, depending on its connection level.
  • It can range from being an Agent-based Single-User System to being an Agent-based Enterprise System, depending on its deployment scope.
  • ...
  • It can integrate with Agent-based Enterprise Systems for business processes.
  • It can connect to Agent-based Storage Systems for content management.
  • It can link to Agent-based Analytics Platforms for performance tracking.
  • It can interface with Agent-based Signature Services for execution handling.
  • It can support Agent-based Compliance Tools for requirement checking.
  • It can follow Agent-based System Protocols for inter-agent communication.
  • It can maintain Agent-based System State through agent coordination.
  • It can implement Agent-based System Architecture patterns.
  • It can support Agent-based System Analysis for performance evaluation.
  • ...
• Examples:
  • AI Agent-Powered Workflows, such as:
    • ...
  • Agent-based Intelligence Systems, such as:
    • Agent-based Learning Systems for adaptive behavior.
    • Agent-based Decision Systems for rational choice.
    • Agent-based Problem-Solving Systems for complex task resolution.
  • Agent-based Collaborative Systems, such as:
    • Agent-based Multi-Agent Learning Systems, such as:
      • Agent-based Swarm Intelligence Systems for collective optimization.
      • Agent-based Distributed Problem-Solving Systems for collaborative task handling.
      • Agent-based Team Systems for coordinated actions.
    • Agent-based Coordination Systems, such as:
      • Agent-based Communication Systems for message exchange.
      • Agent-based Resource Management Systems for resource allocation.
      • Agent-based Task Distribution Systems for workload balancing.
  • Domain-Specific Agent-based Systems, such as:
    • Agent-based Business Systems, such as:
      • Agent-based Contract Systems for contract management.
      • Agent-based Supply Chain Systems for logistics optimization.
      • Agent-based Trading Systems for market analysis.
    • Agent-based Scientific Systems, such as:
      • Agent-based Simulation Systems for ecosystem modeling.
      • Agent-based Research Systems for data analysis.
      • Agent-based Laboratory Systems for experiment automation.
    • Agent-based Industrial Systems, such as:
      • Agent-based Manufacturing Systems for production control.
      • Agent-based Quality Control Systems for inspection automation.
      • Agent-based Maintenance Systems for predictive monitoring.
  • Advanced Agent-based Application Systems, such as:
    • Agent-based Specialized Intelligence Systems, such as Agent-based Blockchain Systems for distributed execution
    • Agent-based Interactive Systems, such as:
      • Agent-based Game Systems for multi-player simulation.
      • Agent-based Virtual Systems for digital environment interaction.
      • Agent-based Social Simulation Systems for behavior modeling.
    • Agent-based Service Systems, such as:
      • Agent-based Healthcare Systems for patient care coordination.
      • Agent-based Security Systems for threat detection.
      • Agent-based Environmental Systems for ecological monitoring.
  • Agent-based Content-Centric Systems, such as:
    • Agent-based Content Management Systems.
    • Agent-based Content Processing Systems.
    • Agent-based Content Workflow Systems.
  • Agent-based Knowledge-Centric Systems, such as:
    • Agent-based Knowledge Discovery Systems.
    • Agent-based Knowledge Management Systems.
    • Agent-based Knowledge Application Systems.
  • ...
• Counter-Examples:
  • Workflow Engine-based Software System (using a workflow engine).
  • Task Tracking Systems, which lack contract specialization.
• See: Agent-based Tightly-coupled System, Agent-based Loosely-coupled System, Agent-based Complex System, Agent-based Multi-Agent System, Agent-based Distributed System, Agent Communication, Agent Coordination, Agent-based Swarm Intelligence.

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References

2024-11-24

• Perplexity.ai
  • Question: What are agent-based software systems? Please explain their architecture, components, and applications. Include how these systems use agent communication protocols, resource management, and coordinated behaviors to maintain system dynamics and adaptability.
  • Answer: Agent-based software systems are complex computational models that utilize multiple interacting software agents to achieve system-wide goals. These systems are characterized by their ability to simulate and solve complex problems through the collective behavior of autonomous agents.
    • System Architectures:
      • Design Types:
        • Centralized vs Distributed: Systems may have a central controlling agent or operate in a fully distributed manner
        • Static vs Dynamic: The number and properties of agents can be fixed or change over time
        • Homogeneous vs Heterogeneous: Agents may have identical capabilities or diverse specializations
    • Communication and Interaction:
      • Protocols:
        • Standardized methods for agent communication
        • Coordination protocols for multi-agent interaction
      • Resource Management:
        • Resource allocation mechanisms
        • Resource sharing protocols
        • Distribution systems for resources
      • Coordinated Behaviors:
        • Rules and algorithms for collective action
        • Protocols for team coordination
    • Applications:
      • Optimization and Problem-Solving:
        • Swarm Intelligence for complex optimization
        • Multi-Agent Learning for collaborative solutions
        • Distributed search capabilities
      • Simulation and Modeling:
        • Social Networks modeling
        • Ecosystems simulation
        • Economic Systems modeling
      • Robotics and Physical Systems:
        • Coordinated Robotic Tasks
        • Collaborative Physical Operations
      • Business and Organizational Management:
        • Supply Chain Optimization
        • Organizational Behavior Studies
    • System Advantages:
      • Emergent Behaviors:
        • Capture complex patterns from simple agent interactions
      • Adaptability:
        • Scalable and modifiable for changing requirements
      • Scenario Testing:
        • Controlled environment for scenario exploration
    • Modeling Techniques:
      • Rule-Based Modeling:
        • Predetermined rules for behavior
        • Interaction rules definition
      • Learning Algorithms:
        • Machine learning integration
        • Reinforcement learning for adaptation
      • Network Modeling:
        • Network representation of agents
        • Complex relationship modeling
  • Citations:

[1] https://en.wikipedia.org/wiki/Agent-based_model
[2] https://botpress.com/blog/real-world-applications-of-ai-agents
[3] https://www.mdpi.com/2078-2489/11/5/271
[4] https://builtin.com/articles/agent-based-modeling

2023

• GBard
  • An agent is a system that can perceive its environment, make decisions and take actions to achieve a specific goal or set of goals. The agent operates autonomously, meaning it is not directly controlled by a human operator.
  • The four rules that all AI agents must adhere to are:
    • Agent Perception: The agent must be able to perceive the environment. This means it must have sensors that can collect information about the environment, such as cameras, microphones, and sensors that measure temperature, humidity, and light.
    • Agent Decision-making: The agent must be able to make decisions based on the information it perceives. This means it must have a decision-making algorithm that can weigh the pros and cons of different actions and choose the one that is most likely to achieve the goal.
    • Agent Action: The agent must be able to take actions to change the environment. This means it must have actuators that can perform actions, such as moving its limbs, driving a car, or controlling a robot.
    • Agent Rational action: The agent's actions must be rational. This means they must be based on the agent's understanding of the environment and its goals.
  • In addition to these four rules, there are other characteristics that are often associated with intelligent agents. These include:
    • Agent Adaptability: The ability to learn and adapt to changes in the environment.
    • Agent Sociability: The ability to interact with other agents and learn from them.
    • Agent Creativity: The ability to generate new ideas and solutions to problems.
  • The concept of an intelligent agent is a broad one, and there are many different types of agents that have been developed. Some examples of intelligent agents include:
    • Game playing agents: These agents are designed to play games against humans or other agents.
    • Search agents: These agents are designed to find information or objects in a large search space.
    • Planning agents: These agents are designed to plan sequences of actions to achieve a goal.
    • Robotic agents: These agents are designed to control robots and other physical systems.
    • Virtual agents: These agents are designed to interact with humans in a virtual environment.

2022

• (Sutton, 2022) => Richard S. Sutton. (2022). “The Quest for a Common Model of the Intelligent Decision Maker". In: Proceedings of the Fifth Multi-disciplinary Conference on Reinforcement Learning and Decision Making
  • ABSTRACT: The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is that multiple disciplines share an interest in goal-directed decision making over time. The idea of this paper is to sharpen and deepen this premise by proposing a perspective on the decision maker that is substantive and widely held across psychology, artificial intelligence, economics, control theory, and neuroscience, which I call the "common model of the intelligent agent". The common model does not include anything specific to any organism, world, or application domain. The common model does include aspects of the decision maker's interaction with its world (there must be input and output, and a goal) and internal components of the decision maker (for perception, decision-making, internal evaluation, and a world model). I identify these aspects and components, note that they are given different names in different disciplines but refer essentially to the same ideas, and discuss the challenges and benefits of devising a neutral terminology that can be used across disciplines. It is time to recognize and build on the convergence of multiple diverse disciplines on a substantive common model of the intelligent agent.

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