Software-based Agent System

A Software-based Agent System is an autonomous agent that is an autonomous software system that performs autonomous tasks on behalf of system users and software systems.

• AKA: Virtual Digital Agent.
• Context:
  • It can (typically) provide System Autonomy through independent operations and automated decisions.
  • It can (typically) maintain Software Agent Behavior via continuous monitoring and proactive actions.
  • It can (typically) support Software Agent Communication with interaction protocols and message exchanges.
  • It can (often) integrate with Software Environments through web systems and mobile platforms.
  • It can (often) process Software Agent Input from user interaction and system events.
  • It can (often) generate Software Agent Output through automated responses.
  • ...
  • It can range from being a Simple Software Agent System to being a Complex Software Agent System, depending on its software agent structural complexity.
  • It can range from being a Reactive Software Agent to being a Proactive Software Agent, depending on its agent behavior pattern.
  • It can range from being a Standalone Software Agent to being a Collaborative Software Agent, depending on its agent interaction mode.
  • It can range from being a Batch Software Agent to being an Interactive Software Agent, depending on its software agent interaction mode.
  • It can range from being a Single-Purpose Software Agent to being a Multi-Purpose Software Agent, depending on its software agent functionality scope.
  • It can range from being a Rule-Based Software Agent to being a Intelligent Software Agent (such as a self-learing software agent), depending on its software agent intelligence level.
  • It can range from being a Domain-Specific Software Agent to being a General-Purpose Software Agent, depending on its task domain coverage.
  • ...
  • It can be a part of an Agent-based System.
  • It can follow a Software Agent Architecture Pattern.
  • It can maintain Software Agent Audit Trails of system actions.
  • It can be part of an Agent-based System in various application domains.
  • It can undergo Agent Behavior Analysis for performance evaluation.
  • ...
• Examples:
  • General-Purpose Software Agent Systems:
    • Personal Assistant Software Agents, such as:
      • Siri Software Agent (2024), providing voice-based interaction
      • Google Assistant Software Agent (2024), offering multi-modal interaction
      • Alexa Software Agent (2024), delivering home automation control
      • ...
    • Conversational Software Agents, such as:
      • Customer Support Software Agents, handling user query
      • Sales Process Software Agents, managing sales interaction
      • Technical Support Software Agents, providing technical guidance
      • ...
  • Infrastructure Software Agent Systems:
    • Web-based Software Agents, such as:
      • Web Crawler Software Agents for content indexing
      • Content Aggregation Software Agents for information collection
      • Website Monitoring Software Agents for system maintenance
      • ...
    • Security Software Agents, such as:
      • Network Monitoring Software Agents for system protection
      • Threat Detection Software Agents for security analysis
      • Access Control Software Agents for permission management
      • ...
  • Domain-Specific Software Agent Systems:
    • Financial-Domain Software Agents, such as:
      • Trading Software Agents for market analysis
      • Portfolio Management Software Agents for investment optimization
      • Risk Assessment Software Agents for compliance monitoring
      • ...
    • Healthcare-Domain Software Agents, such as:
      • Medical Diagnosis Software Agents for clinical analysis
      • Patient Monitoring Software Agents for health tracking
      • Treatment Planning Software Agents for care coordination
      • ...
    • Legal-Domain Software Agents, such as:
      • Legal Research Software Agents for case analysis
      • Document Review Software Agents for contract review
      • Compliance Monitoring Software Agents for regulation tracking
      • ...
  • ...
• Counter-Examples:
  • Static Distributed Systems, which focus on system distribution without necessarily incorporating agent behaviors.
  • Interactive Systems, such as automated teller machines
  • Mechanical-based Agents, such as home robots
  • Human Agents, which require human intelligence
  • Manual Software Tools, which lack agent autonomy
  • Non-Autonomous Scripts, which cannot act independently
• See: Agent, Intelligent Agent, Reasoning System, Believable Virtual Agent, Mobile Agent, Robot, Mobile Device, Human-Robot Interaction, Multi-Agent System (MAS), Autonomous Agent, Digital Assistant, Agent Communication Protocol, Agent Learning Algorithm, Agent-Based Modeling.

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References

2024-11-23

• Perplexity.ai
  • Question: What is a Software Agent System? Please explain its core characteristics, classifications, and common applications. Include information about how these systems operate autonomously, their various types (from simple to complex agents), and provide examples of real-world applications like virtual assistants and security systems. Also, describe their advanced features such as multi-agent capabilities and learning abilities. Focus on their ability to perform automated intelligent tasks and how they differ from regular software systems.
  • Answer: A software agent is a computer program that acts autonomously on behalf of software agent users or other software programs, demonstrating intelligent behavior through perception, reasoning, and action.
    • Core Characteristics:
      • Fundamental Attributes:
        • Autonomy in decision-making and task execution
        • Persistence in continuous operation
        • Social ability to interact with other software system components
        • Reactivity to environmental changes
      • Key Capabilitys:
        • Environmental perception of environmental conditions
        • Action-taking abilities to affect their software agent environment
        • Reasoning to interpret software agent data and determine software agent actions
        • Task delegation from software agent users
        • Communication with humans and other software agents
    • Agent Classifications:
      • Simple Reflex Agents:
        • Follow predefined rules for decision-making based on current software agent situations
        • Ideal for stable environments with straightforward actions
      • Goal-Based Agents:
        • Work towards specific software agent objectives using search algorithms
        • Excel in complex software agent tasks like robotics and natural language processing
      • Utility-Based Agents:
        • Make software agent decisions by maximizing utility functions
        • Effective in resource allocation and scheduling tasks
      • Learning Agents:
        • Continuously improve software agent performance through software agent experience and adaptation
        • Use feedback mechanisms to enhance decision-making capabilities
    • Multi-Agent System Features:
      • Key Features:
        • Distributed architecture with multiple interacting software agents
        • Specialized capabilities for different software agent tasks
        • Collaborative problem-solving abilities
      • Multi-Agent Benefits:
        • Enhanced modularity and scalability
        • Specialized task handling
        • Improved system robustness
        • Distributed processing capabilities
    • Real-World Applications:
      • Security Systems:
        • System monitoring and surveillance
        • Access control management
        • Incident response coordination
        • Data analysis and reporting
      • Virtual Assistant Applications:
        • Banking and financial services
        • Customer service support
        • Healthcare management
        • Government services
    • Advanced Features:
      • Intelligence Capabilitys:
        • Natural Language Processing
        • Pattern Recognition
        • Decision-Making Abilities
        • Learning and Adaptation
      • Software Agent Memory Systems:
        • Operational memory for interaction history
        • Shared memory for multi-agent collaboration
        • Personal memory for individual software agent characteristics
    • System Distinctions:
      • Software Agents differ from conventional software programs through:
        • Autonomous operation without direct user intervention
        • Continuous monitoring and decision-making
        • Goal-oriented behavior
        • Adaptive responses to environmental changes
  • Citations:

[1] https://en.wikipedia.org/wiki/Software_agent
[2] https://www.simform.com/blog/types-of-ai-agents/
[3] https://stealthagents.com/security-systems-virtual-assistant/
[4] https://relevanceai.com/learn/what-is-a-multi-agent-system
[5] https://map.sdsu.edu/geoagent/agent_intro.htm
[6] https://www.sestek.com/top-10-applications-of-virtual-assistants-blog
[7] https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents
[8] https://aws.amazon.com/what-is/ai-agents/?nc1=h_ls
[9] https://www.forbes.com/sites/joannechen/2024/05/24/the-promise-of-multi-agent-ai/

2024

• (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/software_agent Retrieved:2024-6-2.
  • In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency.

    The term agent is derived from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Some agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a computer, such as a mobile device, e.g. Siri. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people (e.g. chatbots, human-robot interaction environments) may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form (see Asimo).

    Related and derived concepts include intelligent agents (in particular exhibiting some aspects of artificial intelligence, such as reasoning), autonomous agents (capable of modifying the methods of achieving their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone), and mobile agents (agents that can relocate their execution onto different processors).

  • NOTES:
    • They are autonomous programs that operate independently, initiating actions and making decisions without direct user commands
    • They can exhibit persistence with continuous monitoring of their environment, enabling dynamic reactions to changes rather than executing only when invoked
    • They can demonstrate goal-oriented behavior, selecting and prioritizing tasks to achieve objectives without human intervention
    • They can possess social abilities, communicating and collaborating with other agents and humans to accomplish complex tasks
    • They can function within multi-agent systems, coordinating with other agents to achieve outcomes that single agents cannot accomplish alone
    • They are defined by their behavior rather than static methods and attributes, combining reactive, proactive, and adaptive capabilities
    • They operate in distributed environments, executing across multiple physical machines and relocating their execution as needed
    • They can maintain their own thread of control while utilizing shared semantics and metadata to ensure consistent data interpretation
    • They can modify their methods of achieving objectives, demonstrating learning and adaptation capabilities over time
    • They can span diverse domains from e-commerce to security, requiring deep understanding of user preferences while raising privacy considerations

2024

• Perplexity
  • It performs automated tasks on behalf of a user or another program with a certain degree of autonomy.
  • It can execute predefined tasks or functions based on specific conditions or events, often without direct human intervention.
  • It ranges from simple scripts that automate repetitive tasks to complex systems employing advanced AI and machine learning algorithms.
  • It exhibits autonomy, making decisions and performing tasks independently without continuous human guidance.
  • It can perceive its environment and respond to changes appropriately, demonstrating reactivity.
  • It can communicate and collaborate with other agents or systems, displaying social ability.
  • It enhances efficiency, reduces manual workloads, and improves task accuracy and speed through automation and data-driven decision-making.

2013

• http://en.wikipedia.org/wiki/Software_agent
  • QUOTE: In computer science, a systems agent is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate.

    Related and derived concepts include intelligent agents (in particular exhibiting some aspect of artificial intelligence, such as learning and reasoning), autonomous agents (capable of modifying the way in which they achieve their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that do not have the capabilities to achieve an objective alone and thus must communicate), and mobile agents (agents that can relocate their execution onto different processors).

2011

• (Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Agent.” In: (Sammut & Webb, 2011) p.36
  • QUOTE: In computer science, the term “agent” usually denotes a software abstraction of a real entity which is capable of acting with a certain degree of autonomy. For example, in artificial societies, agents are software abstractions of real people, interacting in an artifical, simulated environment. Various authors have proposed different definitions of agents. Most of them would agree on the following set of agent properties:
    • Persistence: Code is not executed on demand but runs continuously and decides autonomously when it should perform some activity.
    • Social ability: Agents are able to interact with other agents.
    • Reactivity: Agents perceive the environment and are able to react.
    • Proactivity: Agents exhibit goal-directed behavior and can take the initiative.

2010

• (Akbari, 2010) ⇒ O. Zohreh Akbari. (2010). “A Survey of Agent-Oriented Software Engineering Paradigm: Towards its Industrial Acceptance.” In: Journal of Computer Engineering Research, Vol. 1(2), pp. 14–28.
  • NOTES:
    • Agent-oriented software engineering (AOSE) is a paradigm that uses software agents as central abstractions to analyze, design, and build complex systems.
    • It is particularly suitable for addressing new software development challenges, such as distributed and adaptive systems.
    • Despite extensive research, AOSE has experienced a slow industrial adoption compared to other software engineering paradigms. industrial relevance.
    • AOSE methodologies integrate concepts from other paradigms such as object-oriented, knowledge-based, and service-oriented methodologies, while extending their capabilities.
    • AOSE promotes the use of situational method engineering (SME) to build project-specific methodologies by reusing and combining elements from existing approaches.
    • Strengths include support for distributed systems, social agent behavior, and goal-directed reasoning.
    • Weaknesses include the lack of programming languages tailored to AOSE and the high cost of training development teams.
    • Industrial adoption requires addressing challenges such as standardization and methodology evaluation for broader acceptance.
    • A strategy to integrate AOSE with CMM Level 5 practices is proposed to align the paradigm with organizational goals.

1996

• (Franklin & Graesser, 1996) ⇒ Stan Franklin, and Art Graesser. (1996). “Is It An Agent, Or Just a Program?: A Taxonomy for Autonomous Agents.” In: Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages.
  • NOTES:
    • They are defined as systems situated within an environment, capable of sensing and acting on it autonomously.
    • They act proactively by pursuing goals and taking initiative to achieve tasks beyond mere reactions.
    • They facilitate communication by interacting with other agents or users through defined protocols and languages.
    • They adapt to their environment and refine their behavior over time, utilizing learning mechanisms.
    • They differ from traditional programs by combining autonomy, proactivity, reactivity, and communication.

1996

• (Nwana, 1996) => H. S. Nwana. (1996). “Software Agents: An Overview.” In: Knowledge Engineering Review, 11(3).

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