AI Agent-based Software System

An AI Agent-based Software System is an AI-based software system that uses one or more software agents (to perform automated workflows).

• AKA: Agentic AI System.
• Context:
  • Task Input: Agent Task, System Instructions, Environmental Data
    • Optional Input: Agent Parameters, Task Constraints, Agent Goals
  • Task Output: Agent Actions, Task Results, Agent States
  • Task Performance Measure: System Metrics such as processing efficiency, agent effectiveness, and task completion rate
  • ...
  • It can (typically) implement Agent-based Architecture with agent perception modules, agent decision making modules, and agent action modules.
  • It can (typically) maintain Agent-based Knowledge Base with agent domain knowledge, agent task knowledge, and agent learning history.
  • It can (typically) process Agent Environmental Inputs through agent sensor interfaces and agent data processing pipelines.
  • It can (typically) generate Agent Responses via agent action selection and agent execution modules.
  • It can (typically) utilize Agent Learning Mechanisms for agent performance improvement and agent behavioral adaptation.
  • It can (often) employ Agent Coordination for complex task management.
  • It can (often) implement Agent Communication Protocols for inter-agent interaction.
  • It can (often) use Agent Monitoring Systems for performance tracking and behavioral analysis.
  • It can (often) integrate Agent Safety Mechanisms for risk management and error prevention.
  • It can (often) support Agent Customization through configuration interfaces and parameter adjustments.
  • ...
  • It can range from being a Simple Agent System to being an Advanced Agent System, depending on its system sophistication.
  • It can range from being a Centralized Agent System to being a Distributed Agent System, depending on its architectural pattern.
  • It can range from being a Simple-Task Agent System to being a Complex-Task Agent System, depending on its task complexity.
  • It can range from being a Specialized Agent System to being a General-Purpose Agent System, depending on its capability scope.
  • It can range from being a Human-Operated Agent System to being a Self-Governing Agent System, depending on its autonomy level.
  • It can range from being a Static-Knowledge Agent System to being an Evolving-Knowledge Agent System, depending on its learning capability.
  • It can range from being a Single-Domain Agent System to being a Cross-Domain Agent System, depending on its domain coverage.
  • It can range from being a Reactive Agent System to being a Proactive Agent System, depending on its behavioral 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 Supervised Learning Agent System to being a Self-Learning Agent System, based on its learning autonomy.
  • It can range from being a Single Agent-based System to being a Multi Agent-based System, depending on its agent composition.
  • It can range from being a Deterministic Agent-based System to being a Stochastic Agent-based System, depending on its decision model.
  • It can range from being a Local Agent-based System to being a Cloud Agent-based System, depending on its deployment environment.
  • ...
• Examples:
  • Agent Architecture Types, such as:
    • Reactive Agent Systems, such as:
      • Simple Reflex Agent Systems for direct responses.
      • State-Based Agent Systems for contextual actions.
      • Model-Based Agent Systems for environmental modeling.
    • Deliberative Agent Systems, such as:
      • Planning Agent Systems for goal achievement.
      • Learning Agent Systems for adaptive behavior.
      • Reasoning Agent Systems for complex decision making.
  • Agent Interaction Patterns, such as:
    • Single Agent Systems, such as:
      • Task-Specific Agent Systems for focused operations.
      • General-Purpose Agent Systems for diverse tasks.
    • Multi-Agent Systems, such as:
      • Collaborative Agent Networks for team tasks.
      • Distributed Agent Clusters for parallel processing.
  • Domain-Specific Agent Systems, such as:
    • Industrial Agent Systems, such as:
      • Manufacturing Agent Systems for production control.
      • Quality Control Agent Systems for inspection automation.
      • Maintenance Agent Systems for predictive monitoring.
    • Financial Agent Systems, such as:
      • Trading Agent Systems for market operations.
      • Risk Assessment Agent Systems for portfolio analysis.
      • Fraud Detection Agent Systems for security monitoring.
    • Healthcare Agent Systems, such as:
      • Diagnostic Agent Systems for medical analysis.
      • Patient Monitoring Agent Systems for health tracking.
      • Treatment Planning Agent Systems for care optimization.
    • Scientific Agent Systems, such as:
      • Research Agent Systems for data analysis.
      • Simulation Agent Systems for model testing.
      • Laboratory Agent Systems for experiment control.
  • ...
• Counter-Examples:
  • Traditional Software System, which uses fixed logic instead of agent-based decision making.
  • Manual Task System, which requires constant human intervention for operation.
  • Rule-Based Software System, which operates on predefined rules without learning capability.
  • Static Processing Software System, which cannot adapt to environmental changes.
  • Single Function System, which lacks agent interaction capabilities.
• See: Software Agent, Agent Architecture, Multi-Agent System, Agent Communication, Agent Learning System, Autonomous System, Intelligent System.

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• • ...
• Example(s):
  • 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-Example(s):
  • 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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