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Intelligent Agents CPS 4801-01.

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Presentation on theme: "Intelligent Agents CPS 4801-01."— Presentation transcript:

1 Intelligent Agents CPS

2 Outline Agents and environments
PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types

3 Agents An AI program = An intelligent Agent
An agent is anything that can be viewed as perceiving its environment though sensors and acting upon that environment though actuators. Perception-Action Cycle

4 Application of Intelligent Agents
AI has successfully been used in Finance? Robotics? Games? Medicine? The Web?

5 AI in Finance Trading Agent Rates Stock Market Bonds News
Commodities Market Trading Agent Rates News Trades

6 AI in Robotics Physical actuators Cameras Microphones Robot
Environment Robot Cameras Microphones Touch Motors: wheels, legs, arms, grippers Voice

7 AI in Games Chess game Characters in games Game Agent Your moves You
Its own moves

8 AI in Medicine You Diagnostic Agent Vital Signals: blood pressures,
heart signals Diagnostics Doctor

9 Simple Diagnostic Expert System:

10 AI and the Web World Wide Web Crawler Web Pages DB Query You

11 Percept: the agent’s perceptual inputs at any given instant
Percept Sequence: the complete history of everything the agent has ever perceived The agent function maps from percept histories to actions: [f: P*  A] (abstract) The agent program runs on the physical architecture to produce f. (implementation)

12 Vacuum-Cleaner World Percepts: location and contents, e.g., [A, Dirty]
Actions: Left, Right, Suck, NoOp

13 A Vacuum-Cleaner Agent
Percept sequence Action [A, Clean] Right [A, Dirty] Suck [B, Clean] Left [B, Dirty] [A, Clean] [A, Clean] [A, Clean] [A, Dirty] [A, Clean] [A, Clean] [A, Clean] [A, Clean] [A, Clean] [A, Dirty]

14 A Vacuum-Cleaner Agent
function REFLEX-VACUUM-AGENT ([location,status]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left

15 Task Environment PEAS: Performance measure, Environment, Actuators, Sensors Consider the task of designing an automated taxi: Performance measure: safety, destination, profits, legality, comfort… Environment: US streets/freeways, traffic, pedestrians, weather… Actuators: steering, accelerator, brake, horn, speaker/display… Sensors: camera, sonar, GPS, odometer, engine sensor…

16 Internet Shopping Agent
Performance measure? Environment? Actuators? Sensors?

17 Internet Shopping Agent
Performance measure? price, quality, appropriateness, efficiency Environment? WWW sites, vendors, shippers Actuators? display to user, follow URL, fill in form Sensors? HTML pages (text, graphics, scripts)

18 Environment Types Fully observable (vs. partially observable): An agent’s sensors give it access to the complete state of the environment at each point in time. Card game vs. poker (needs internal memory) Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. Chess vs. game with dice (uncertainty, unpredictable) Episodic (vs. sequential): The agent’s experience is divided into atomic “episodes” (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself. Chess and taxi driving

19 Environment Types Static (vs. dynamic): The environment is unchanged while an agent is deliberation. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent’s performance score does.) Taxi driving vs. chess (when played with a clock) vs. crossword puzzles Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. Chess vs. taxi driving (infinite) Single agent (vs. multiagent): An agent operating by itself in an environment. Crossword puzzle vs. chess

20 Solitaire Chess with a clock Internet Shopping Taxi Observable? Deterministic? Episodic? Static? Discrete? Single-agent?

21 Solitaire Chess with a clock Internet Shopping Taxi Observable? Yes No Deterministic? Episodic? Static? Semi Discrete? Single-agent? Yew The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent.

22 Agent Types Four basic types: - simple reflex agents
- model-based reflex agents - goal-based agents - utility-based agents All these can be turned into learning agents.

23 Simple Reflex Agents

24 Model-Based Reflex Agents

25 Goal-Based Agents

26 Utility-Based Agents

27 Learning Agents

28 Summary Agents interact with environments though actuators and sensors. The agent function describes what the agent does in all circumstances. Agent programs implement agent functions. PEAS descriptions define task environments. Environment are categorized along several dimensions: observable? deterministic? episodic? static? discrete? single-agent? Several basic agent architectures exist: reflex, reflex with state, goal-based, utility-based

29 Try out some intelligent agents!
A Chatbot is a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods, primarily for engaging in small talk. ALICE: Won the Loebner Prize three times (in 2000, 2001 and 2004) ELIZA: One of the classic chat bots, written at MIT by Joseph Weizenbaum between 1964 and 1966 Automated online assistants

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