Intelligent (Knowledge Based) agent in Artificial Intelligence
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The document provides an overview of intelligent agents, defining them as systems that perceive their environment through sensors and act through effectors. It discusses various types of agents, their architectures, and the concepts of autonomy and rationality, highlighting the difference between perfect and bounded rationality. Additionally, it introduces the PEAS (Performance measure, Environment, Actuators, Sensors) framework for agent design and describes various examples of intelligent agents in different contexts.
![Agents and Environments
• The agent function maps from percept histories to actions:
[f: P A]
• The agent program runs on the physical architecture to
produce f
• f = agent = architecture + program](https://image.slidesharecdn.com/intelligentagent-210309041632/85/Intelligent-Knowledge-Based-agent-in-Artificial-Intelligence-6-320.jpg)
![Table-driven Agents
A table is a simple way to specify the mapping, [f: P A]
• Information comes from sensors: percepts
• Look it up in a table
• Triggers actions through effectors
• No notion of history. Action determined by current state](https://image.slidesharecdn.com/intelligentagent-210309041632/85/Intelligent-Knowledge-Based-agent-in-Artificial-Intelligence-19-320.jpg)
Intelligent (Knowledge Based) agent in Artificial Intelligence
- 1. Intelligent (Knowledge Based) Agents
- 2. Agent: Definition • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators • Human agent: – Sensors: eyes, ears, and other organs – Actuators: hands, legs, and some body parts • Robotic agent: – Sensors: cameras, range finders, etc. – Actuators: levers, motors, etc. • Softbots (s/w agents)
- 3. Agent Architecture Real World Agent Sensors Effectors Reasoning & Decisions Making Model of World (being updated) List of Possible Actions Prior Knowledge about the World Goals/Utility
- 4. Intelligent Agents • Fundamental abilities of intelligence – Sensing – Understanding and reasoning – Acting • In order to act, it must first sense. Blind action is not intelligent action • To sense wisely, AI system has to understand • Agent must be autonomous • Agent must be rational
- 5. Sensors and Effectors • An agent perceives its environment through sensors – Percept: the complete set of inputs to an agent at a given time – The current percept or a sequence of percepts determines the action of an agent • An agent can change its environment through effectors or actuators – Operation involving an actuator is an action
- 6. Agents and Environments • The agent function maps from percept histories to actions: [f: P A] • The agent program runs on the physical architecture to produce f • f = agent = architecture + program
- 7. Agent Functions • An agent is completely specified by the agent function that maps percept sequences to actions • Find a way to implement the agent function concisely • An agent program implements the above mapping (ie, from percept sequences to actions) Agent’s Performance • Behavior of Agent: In terms of agent function: – Mapping: “Perception history into Action” – Ideal Mapping: the sequence of actions the agent ought to take at any point in time • Performance of Agent: a subjective measure to characterize how successful an agent is performing in terms of – Power consumption, accuracy, profit, etc.
- 8. Setting of Agent: PEAS • Specification of the setting for intelligent agent design has 4 coordinates: PEAS –Performance measure –Environment –Actuators –Sensors
- 9. PEAS: Example 1 • Automated taxi driver: – Performance measure: Safe, fast, legal, comfortable trip, maximize profits – Environment: Roads, other traffic, pedestrians, customers – Actuators: Steering wheel, accelerator, brake, signal, horn – Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard
- 10. PEAS: Example 2 • Medical diagnosis system: – Performance measure: Healthy patient, minimize costs, lawsuits – Environment: Patient, hospital, staff – Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) – Sensors: Keyboard (entry of symptoms, findings, patient's answers)
- 11. PEAS: Example 3 • Part-picking robot: – Performance measure: Percentage of parts in correct bins – Environment: Conveyor belt with parts, bins – Actuators: Jointed arm and hand – Sensors: Camera, joint angle sensors
- 12. PEAS: Example 4 • Interactive English tutor: – Performance measure: Maximize student's score on test – Environment: Set of students – Actuators: Screen display (exercises, suggestions, corrections) – Sensors: Keyboard
- 13. task environm ent observable deterministic/ stochastic episodic/ sequential static/ dynamic discrete/ continuous agents crossword puzzle fully determ. sequential static discrete single chess with clock fully strategic sequential semi discrete multi taxi driving partial stochastic sequential dynamic continuous multi image analysis fully determ. episodic semi continuous single partpicking robot partial stochastic episodic dynamic continuous single refinery controller partial stochastic sequential dynamic continuous single interact. tutor partial stochastic sequential dynamic discrete multi
- 14. Realistic Environments • The simplest environment is – Fully observable, deterministic, episodic, static, discrete and single-agent. • Most real situations are: – Partially observable, stochastic, sequential, dynamic, continuous and multi-agent.
- 15. Types of Agents Concept • Autonomous agent • Rational agent • Perfect rationality • Bounded rationality Type of Agent – Table-driven agent – Simple reflex agent – Model-based reflex agent – Goal-based agent • Problem-solving agent – Utility-based agent • Can distinguish between different goals – Learning agent
- 16. Autonomous Agent • Autonomous: free, independent, sovereign, not subject to the rule or control of another. • An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt) – An autonomous agent decides autonomously which action to take in the current situation to maximize progress toward a goal
- 17. Rational Agents • An agent should strive to "do the right thing", based on what it can perceive and perform the actions. • For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure using the agent has whatever built-in knowledge. • Rationality is distinct from omniscience (all-knowing with infinite knowledge)
- 18. Perfect Vs Bounded Rationality • Perfect Rationality: Assumes that the rational agent knows all information and takes action that maximizes its utility. – Humans do not satisfy this definition • Bounded Rationality: Because of the limitations of the human mind, humans use approximate methods to handle many tasks.
- 19. Table-driven Agents A table is a simple way to specify the mapping, [f: P A] • Information comes from sensors: percepts • Look it up in a table • Triggers actions through effectors • No notion of history. Action determined by current state
- 20. Drawbacks of Table-driven Agents • Huge tables for mapping – Chess needs a table with 35100 entries • Take a long time to build the table by the designer • No autonomy – all actions are pre-determined • Even with learning, need a long time to learn the table entries • Types of tables – Rule based, Neural networks, etc
- 24. State-based Models (Search, Planning) – Solutions are defined as a sequenceof steps – Model the task as a graph of statesand solution as a path – A state captures all the relevant information about the past in order to act (optimally) in the future Applications: Navigation, Games – State-space graphs
- 25. Logic-based Models (Logic) – Implicit representation of classes of objects – Deductive reasoning Applications: Question answering systems, natural language understanding – Propositional logic, First-order logic
- 26. References • Tom Markiewicz & Josh Zheng, Getting started with Artificial Intelligence, Published by O’Reilly Media,2017 • Stuart J. Russell and Peter Norvig, Artificial Intelligence A Modern Approach