Artificial Intelligence (AI) Agent

An Artificial Intelligence (AI) Agent is an autonomous agent that is an AI system (which can perform autonomous intelligence task).

• AKA: Autonomous AI Agent.
• Context:
  • Input(s): sensory data, instructions, environmental state.
    • Optional: goal parameters, behavioral constraints.
  • Output(s): agent decisions, actions, state updates.
  • Performance Measures, such as task success rate and response time.
  • ...
  • It can range from being a Basic AI Agent to being an Advanced AI Agent, depending on its capability level.
  • It can range from being a Task-Specific AI Agent to being a General-Purpose AI Agent, depending on its functional scope.
  • It can range from being a Human-Guided AI Agent to being a Self-Directed AI Agent, depending on its autonomy level.
  • It can range from being a Fixed-Knowledge AI Agent to being an Adaptive AI Agent, depending on its learning capability.
  • It can range from being a Simple-Intelligence AI Agent to being a Complex-Intelligence AI Agent, depending on its cognitive sophistication.
  • It can range from being a Narrow-Domain AI Agent to being a Multi-Domain AI Agent, depending on its domain expertise.
  • It can range from being a Single-Purpose AI Agent to being a Multi-Purpose AI Agent, depending on its functional versatility.
  • It can range from being a Solo-Operating AI Agent to being a Collaborative AI Agent, depending on its interaction pattern.
  • ...
  • It can be a component of an AI Agent-based System.
  • It can be deployed in virtual environments such as simulated worlds and digital platforms to physical environments like robotics systems, autonomous vehicles, or smart cities.
  • It can desire and achieve Agent Goals/Desired Results in a wide range of environments.
  • It can manifest Goal-Direct Behaviors (perform Decision Making to achieve a Desired Result).
  • It can be evaluated by an AI Agent Evaluation Task, such as AI agent benchmarking.
  • It can be based on an AI Agent Platform.
  • It can belong to an Intelligent Agent Community.
  • It can perform an Intelligent Agent Action.
  • It can create Abstract Constructs.
  • ...
• Example(s):
  • A Collective Intelligent Agent, such as a team.
  • Contemporary AI Agents, such as:
    • Large Language Model Agents for natural language tasks
    • Multimodal AI Agents that combine text, vision, and speech
    • Embodied AI Agents in robotic systems
  • …
• Counter-Example(s):
  • a Non-Intelligent Agent, such as a web crawler.
  • a Non-Autonomous Intelligent System, such as a Watson Q/A system.
• See: Cognitive Agent, Moral Agent, Cognitive Agent, Experty System.

---

---

References

2014

• https://www.udacity.com/wiki/cs271/unit1-notes#intelligent-agents
  • QUOTE: Properties of an intelligent agent:
    • interacts with an environment in a state
    • uses sensors to perceive its state.
    • uses actuators to affect its state.
    • has a function called its control policy that maps sensors to actuators.
  • This class will deal with how an agent makes decisions that it can carry out with its actuators based on past sensor data. The loop of environment feedback to sensors, agent decision, actuator interaction with the environment and so on is called perception action cycle.

2013

• http://en.wikipedia.org/wiki/Intelligent_agent
  • In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.

    Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA)^{[citation needed]} to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents. Still others considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, “rational agent”.

    Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.

    Intelligent agents are also closely related to software agents (an autonomous computer program that carries out tasks on behalf of users). In computer science, the term intelligent agent may be used to refer to a software agent that has some intelligence, regardless if it is not a rational agent by Russell and Norvig's definition. For example, autonomous programs used for operator assistance or data mining (sometimes referred to as bots) are also called "intelligent agents".

2009

• (Wooldridge, 2009) ⇒ Michael Wooldridge. (2009). “An Introduction to MultiAgent Systems, 2nd edition.” In: Wiley Publishing. ISBN:0470519460, ISBN:9780470519462.
  • QUOTE: The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). … Multiagent systems are systems composed of multiple interacting computing elements, known as agents.

---