Agent-based Software System

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

• AKA: Agent System.
• Context:
  • It can (typically) support Agent Interaction through agent communication protocols
  • It can (typically) maintain System Dynamics via agent behaviors
  • It can (typically) achieve System Goals through collective actions
  • It can (often) enable Agent Collaboration via shared environments
  • It can (often) manage Agent Resources across system components
  • It can (often) coordinate Agent Activity through system protocols
  • ...
  • 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 System to being a Loosely-coupled Agent System, depending on its agent coupling.
  • It can range from being a Centralized Agent System to being a Distributed Agent System, depending on its system architecture.
  • It can range from being a Homogeneous Agent System to being a Heterogeneous Agent System, depending on its agent diversity.
  • It can range from being a Static Agent System to being a Dynamic Agent System, depending on its system adaptability.
  • It can range from being a Reactive Agent System to being a Proactive Agent System, depending on its agent behavior pattern.
  • It can range from being a Single-Goal Agent System to being a Multi-Goal Agent System, depending on its goal complexity.
  • It can range from being a Limited-Environment Agent System to being an Open-Environment Agent System, depending on its environmental scope.
  • It can range form being a Domain-Specific Agent Systems to being Open-Domain Agent Systems, depending on ... ....
  • ...
  • It can follow Agent System Protocols for inter-agent communication.
  • It can maintain System State through agent coordination.
  • It can implement Agent System Architecture patterns.
  • It can support Agent System Analysis for performance evaluation.
  • ...
• Examples:
  • Intelligence-based Agent Systems, such as:
    • Learning Agent Systems for adaptive behavior
    • Decision-Making Agent Systems for rational choice
    • Problem-Solving Agent Systems for complex task resolution
    • Creative Agent Systems for solution generation
    • ...
  • Collaborative Agent Systems, such as:
    • Multi-Agent Learning Systems, such as:
      • Swarm Intelligence Systems for collective optimization
      • Distributed Problem-Solving Systems for collaborative task handling
      • Team-based Agent Systems for coordinated actions
      • Emergent Behavior Systems for pattern generation
      • ...
    • Agent Coordination Systems, such as:
      • Agent Communication Systems for message exchange
      • Agent Resource Management Systems for resource allocation
      • Agent Task Distribution Systems for workload balancing
      • Agent Negotiation Systems for conflict resolution
      • ...
  • Domain-Specific Agent Systems, such as:
    • Business Agent Systems, such as:
      • Supply Chain Agent Systems for logistics optimization
      • Trading Agent Systems for market analysis
      • Customer Service Agent Systems for interaction handling
      • ...
    • Scientific Agent Systems, such as:
      • Simulation Agent Systems for ecosystem modeling
      • Research Agent Systems for data analysis
      • Laboratory Agent Systems for experiment automation
      • ...
    • Industrial Agent Systems, such as:
      • Manufacturing Agent Systems for production control
      • Quality Control Agent Systems for inspection automation
      • Maintenance Agent Systems for predictive monitoring
      • ...
  • Application-Specific Agent Systems, such as:
    • Game-based Agent Systems for multi-player simulation
    • Search-based Agent Systems for distributed search
    • Planning-based Agent Systems for collaborative planning
    • Robot-based Agent Systems for physical task coordination
    • Virtual Agent Systems for digital environment interaction
    • Social Simulation Systems for behavior modeling
    • Economic Agent Systems for market simulation
    • Healthcare Agent Systems for patient care coordination
    • Security Agent Systems for threat detection
    • Environmental Agent Systems for ecological monitoring
    • ...
• Counter-Example(s):
  • a Single Agent System that operates in isolation without agent interaction
  • a Monolithic System that lacks component autonomy and agent properties
  • a Static Software System that doesn't exhibit dynamic agent behavior
  • a Centrally Controlled System that doesn't allow agent autonomy
• See: Tightly-coupled System, Loosely-coupled System, Complex System, Multi-Agent System, Distributed System, Agent Communication, Agent Coordination, 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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