Artificial Intelligence (AI) Agent
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An Artificial Intelligence (AI) Agent is an autonomous agent that is an AI system (which can perform autonomous intelligence task).
- Context:
- Input(s): sensory data, instructions, environmental state.
- Output(s): agent decisions, actions, state updates.
- Performance Measures, such as task success rate and response time.
- ...
- It can (typically) be composed of AI Agent Components, such as:
- It can (typically) implement Agent Perception System to receive agent world state through agent sensor systems.
- It can (typically) execute Agent Action System to affect its agent environment through agent actuator systems.
- It can (typically) utilize Agent Policy System to select agent behavior actions based on agent current state.
- It can (typically) process Agent Reward System to evaluate agent feedback signals from agent state transitions.
- ...
- It can utilize Sensor Systems to perceive its environmental state.
- It can employ Actuator Systems to affect its environment.
- It can implement Control Policy to map sensor data to actuator actions.
- It can participate in Perception Action Cycles through continuous environment feedback.
- It can utilize Knowledge Representation for goal achievement.
- ...
- It can range from being a Basic AI Agent to being a Advanced AI Agent, depending on its capability sophistication and overall design maturity.
- It can range from being a Task-Specific AI Agent to being a General-Purpose AI Agent, depending on its functional breadth and intended operational scope.
- It can range from being a Narrow-Domain AI Agent to being a Multi-Domain AI Agent, depending on its domain expertise and specialization level.
- It can range from being a Human-Guided AI Agent to being a Self-Directed AI Agent, depending on its autonomy level and decision-making independence.
- It can range from being a Solo-Operating AI Agent to being a Collaborative AI Agent, depending on its interaction pattern and teamwork capability.
- It can range from being a Fixed-Rule AI Agent to being a Adaptive Learning AI Agent, depending on its learning capability and rule update mechanism.
- It can range from being a Opaque AI Agent to being a Transparent AI Agent, depending on its explainability level and internal process visibility.
- It can range from being a Post-Hoc Explainable AI Agent to being a Inherent Explainable AI Agent, depending on its explanation approach and real-time transparency.
- It can range from being a Cooperative Multi AI Agent to being a Competitive Multi AI Agent, depending on its interaction mode and strategic engagement.
- ...
- 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.
- It can participate in Multi Agent Systems with other intelligent agents.
- It can engage in Cooperative Behavior or Competitive Behavior with other agents.
- It can operate in Economic Systems as a rational agent.
- ...
- Examples:
- AI Agent Intelligence Types, such as:
- AI Domain Agents, such as:
- Healthcare AI Agents, such as:
- Legal AI Agents, such as:
- Financial AI Agents, such as:
- Education AI Agents, such as:
- AI Operational Agents, such as:
- AI Environment Agents, such as:
- Virtual AI Agents, such as:
- Physical AI Agents, such as:
- 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
- AI Collaborative Systems, such as:
- ...
- Counter-Examples:
- a Non-Intelligent Agent, such as a web crawler.
- a Non-Autonomous Intelligent System, such as a Watson Q/A system.
- a Passive Software System, which lacks goal-directed behavior.
- a Pure Data Processing System, which lacks autonomous decision making.
- See: Cognitive Agent, Moral Agent, Cognitive Agent, Experty System, Rational Agent, Software Agent, Multi Agent System.
References
2024-01-13
- Perplexity.ai
- Question: What are AI Agents and their core capabilities, types, performance metrics, applications, and advanced capabilities?
- Answer: AI Agents are autonomous systems capable of perceiving their environment, making decisions, and taking actions to achieve specific objectives.
- Core Capabilities:
- Perception and Processing:
- Collect and analyze data from various sources.
- Process environmental inputs and contextual information.
- Use advanced natural language processing for understanding and communication.
- Decision Making:
- Employ sophisticated algorithms for autonomous decision-making.
- Utilize internal models to evaluate options and predict outcomes.
- Make real-time decisions in dynamic environments.
- Action Execution:
- Execute tasks independently or semi-autonomously.
- Interact with external tools and systems.
- Adapt responses based on feedback and learning.
- Perception and Processing:
- Types and Classifications:
- Based on Intelligence Level:
- Simple reflex agents: Operate on basic condition-action rules.
- Model-based agents: Maintain internal world models.
- Goal-based agents: Work toward specific objectives.
- Utility-based agents: Optimize decisions using value functions.
- Learning agents: Improve performance through experience.
- Based on Function:
- Based on Intelligence Level:
- Performance Metrics:
- Efficiency Metrics:
- Response time: Target < 3 seconds.
- Task completion rate: Goal > 85%.
- Error rate: Target < 5%.
- System uptime: Goal > 99.9%.
- Quality Metrics:
- Accuracy of responses.
- Generation quality.
- User satisfaction scores.
- Problem-solving success rates.
- Efficiency Metrics:
- Applications:
- Industry Applications:
- Smart City Implementation:
- Traffic management optimization.
- Public service automation.
- Emergency response coordination.
- Utility network management.
- Advanced Capabilities:
- Tool Integration:
- Use of calculators, APIs, and search engines.
- Integration with various AI systems.
- Multifaceted capabilities combining different AI technologies.
- Learning and Adaptation:
- Continuous improvement through experience.
- Pattern recognition and analysis.
- Complex reasoning techniques.
- Customized output generation.
- Tool Integration:
- Core Capabilities:
- Citations:
[1] https://www.signitysolutions.com/blog/ai-agents [2] https://dialzara.com/blog/how-to-measure-ai-agent-performance/ [3] https://indatalabs.com/blog/ai-agents-examples [4] https://dialzara.com/blog/ai-agent-metrics-what-smbs-need-to-know/ [5] https://www.leewayhertz.com/ai-agents/ [6] https://smythos.com/ai-agents/impact/ai-agent-performance-measurement/ [7] https://www.automationanywhere.com/company/blog/automation-ai/exploring-ai-agents-types-capabilities-and-real-world-applications [8] https://www.restack.io/p/ai-agent-answer-kpis-for-ai-agents-cat-ai [9] https://www.ibm.com/think/topics/ai-agents [10] https://cloud.google.com/transform/gen-ai-kpis-measuring-ai-success-deep-dive [11] https://docs.gorgias.com/en-US/review-ai-agents-performance-in-your-statistics-931052 [12] https://www.salesforce.com/agentforce/what-are-ai-agents/?bc=OTH [13] https://aws.amazon.com/what-is/ai-agents/?nc1=h_ls [14] https://smythos.com/artificial-intelligence/autonomous-agents/autonomous-agents-in-smart-cities/ [15] https://beam.ai/use-cases/ai-agents-pioneers-in-building-smarter-cities [16] https://www.linkedin.com/pulse/unleashing-power-ai-exploring-examples-agents-vijay-kumar--33uyc [17] https://www.engati.com/blog/ai-agents-integrations [18] https://botpress.com/blog/real-world-applications-of-ai-agents [19] https://www.multimodal.dev/post/ai-kpis [20] https://www.usefini.com/post/top-5-customer-support-metrics-improved-by-ai
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.
- QUOTE: Properties of an intelligent agent:
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).[1] 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,[2] 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".
- 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).[1] 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,[2] as is a human being, as is a community of human beings working together towards a goal.
- ↑ Template:Harvnb
- ↑ According to the definition given by Template:Harvtxt
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.