Autonomous (Agent-based) System

An Autonomous (Agent-based) System is a AI-based software system that includes AI-based agents (for performing intelligent autonomous tasks).

• AKA: AI Agent System, Intelligent Agent Platform.
• Context:
  • Input(s): agent task, agent instructions, environmental data.
    • optional: agent parameters, task constraints.
  • Output(s): agent actions, task results.
  • Performance Measure(s): task success rate, agent efficiency, learning progress.
  • ...
  • It can range from being a Simple AI Agent System to being a Complex AI Agent System, depending on its agent complexity.
  • It can range from being a Single AI Agent System to being a Multi-Agent AI System, depending on its agent coordination scope.
  • It can range from being a Reactive AI Agent System to being a Planning AI Agent System, depending on its agent reasoning type.
  • It can range from being a Fixed AI Agent System to being an Adaptive AI Agent System, depending on its learning capability.
  • It can range from being a Limited-Task AI Agent System to being a General-Task AI Agent System, depending on its task scope.
  • It can range from being a Domain-Specific AI Agent System to being a Open-Domain AI Agent System, depending on its domain scope.
  • It can range from being a Human-Guided AI Agent System to being an Autonomous AI Agent System, depending on its autonomy level.
  • ...
  • It can be based on a AI Agent Software Framework.
  • ...
• Examples:
  • AI Agent Architectures, such as:
    • Reactive Agent Systems, such as:
      • Simple Reflex Systems for direct responses
      • State-Based Systems for contextual actions
    • Deliberative Agent Systems, such as:
      • Planning Agent Systems for goal achievement
      • Learning Agent Systems for adaptive behavior
  • Agent Interaction Patterns, such as:
    • Single Agent Systems, such as:
      • Task-Specific Agents for focused operations
      • General-Purpose Agents for diverse tasks
    • Multi-Agent Systems, such as:
      • Collaborative Agent Networks for team tasks
      • Distributed Agent Clusters for parallel processing
  • Agent Intelligence Types, such as:
    • Learning-Based Systems, such as:
      • Supervised Agent Systems for guided learning
      • Reinforcement Agent Systems for autonomous learning
    • Reasoning-Based Systems, such as:
      • Logic Agent Systems for rule processing
      • Probabilistic Agent Systems for uncertainty handling
  • Agent Application Domains, such as:
    • Business Agent Systems, such as:
      • Process Automation Agents for workflow management
      • Decision Support Agents for business analysis
    • Personal Assistant Systems, such as:
      • Task Management Agents for schedule organization
      • Information Agents for knowledge retrieval
  • ...
  • Domain-Specific Agent Systems, such as:
    • Industrial Agent Systems, such as:
      • Manufacturing Agents for production control
      • Robotics Agents for automated operations
      • Quality Control Agents for inspection tasks
    • Financial Agent Systems, such as:
      • Trading Agents for market operations
      • Risk Assessment Agents for portfolio analysis
      • Fraud Detection Agents for security monitoring
    • Healthcare Agent Systems, such as:
      • Diagnostic Agents for medical analysis
      • Patient Monitoring Agents for health tracking
      • Treatment Planning Agents for care optimization
    • Scientific Agent Systems, such as:
      • Research Agents for data analysis
      • Simulation Agents for model testing
      • Laboratory Agents for experiment control
    • Security Agent Systems, such as:
      • Network Security Agents for threat detection
      • Access Control Agents for permission management
      • Incident Response Agents for breach handling
  • ...
• Counter-Examples:
  • Non-AI Agent System.
  • Traditional Software System.
  • Manual Task System.
• See: AI Agent, Agent Architecture, Learning System, Task Automation, Multi-Agent System.

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References

2024-12-01

• Perplexity.ai
  • Question: What are AI agents and their main types? Please explain their key components (sensors, actuators, decision-making systems), different classifications (from simple reflex to learning agents), and real-world applications across industries like robotics, finance, and healthcare. Also describe their core characteristics such as autonomy, perception, and learning capabilities.
  • Answer: AI agents are autonomous software or hardware systems that perceive their environment, make decisions, and take actions to achieve specific goals.
    • Core Components and Characteristics:
      • Architecture Components:
        • Physical or software interfaces for sensing.
        • Actuators for executing actions.
        • Decision-making systems for processing information.
        • Knowledge base and feedback mechanisms.
      • Key Characteristics:
        • Autonomy in decision-making and task execution.
        • Rational decision-making based on perceptions.
        • Ability to adapt to environmental changes.
        • Continuous learning from experiences.
    • Types of AI Agents:
      • Simple Reflex Agents:
        • Use condition-action rules based on current perceptions.
        • Excel in well-structured, predictable tasks.
        • Handle basic automation and simple operations.
      • Model-Based Agents:
        • Maintain internal world models.
        • Consider unseen aspects of environment.
        • Evaluate action consequences.
      • Goal-Based Agents:
        • Utilize search algorithms for efficient paths.
        • Consider future consequences.
        • Follow predefined rules.
      • Utility-Based Agents:
        • Maximize utility functions.
        • Evaluate expected outcomes.
        • Handle complex and uncertain situations.
      • Learning Agents:
        • Include learning, critique, performance, and problem generator components.
        • Improve through experience.
        • Generate new learning opportunities.
    • Real-World Applications:
      • Automation and Robotics:
        • Control manufacturing processes.
        • Operate autonomous vehicles.
        • Manage industrial and warehouse operations.
      • Business and Finance:
        • Detect fraud.
        • Analyze markets.
        • Allocate resources.
        • Automate customer service.
      • Personal Assistance:
        • Provide virtual assistance.
        • Manage schedules.
        • Automate personal tasks.
        • Retrieve information.
    • Advanced Implementations:
      • Hierarchical Agents:
        • Support supervision hierarchy.
        • Enable task decomposition.
        • Monitor progress.
      • Multi-Agent Systems:
        • Coordinate actions.
        • Share information.
        • Solve complex problems collaboratively.
        • Adapt to dynamic environments.
  • Citations:

[1] https://www.simform.com/blog/types-of-ai-agents/
[2] https://www.signitysolutions.com/blog/ai-agents
[3] https://www.automationanywhere.com/company/blog/automation-ai/exploring-ai-agents-types-capabilities-and-real-world-applications
[4] https://www.geeksforgeeks.org/agents-artificial-intelligence/
[5] https://aws.amazon.com/what-is/ai-agents/?nc1=h_ls
[6] https://www.codica.com/blog/brief-guide-on-ai-agents/