Intelligent Agents อาจารย์อุทัย เซี่ยงเจ็น สำนักเทคโนโลยีสารสนเทศและการ สื่อสาร มหาวิทยาลัยนเรศวร วิทยาเขต สารสนเทศพะเยา

Notion of an Agent environment agent ? sensors actuators laser range finder sonars touch sensors

Notion of an Agent environment agent ? sensors actuators Locality of sensors/actuators Imperfect modeling Time/resource constraints Sequential interaction Multi-agent worlds

Example: Tracking a Target target robot The robot must keep the target in view The target’s trajectory is not known in advance The robot may not know all the obstacles in advance Fast decision is required

What is Artificial Intelligence? (revised) Study of design of rational agents agent = thing that acts in environment Rational agent = agent that acts rationally: –actions are appropriate for goals and circumstances to changing environments and goals –learns from experience

Goals of Artificial Intelligence Scientific goal: –understand principles that make rational (intelligent) behavior possible, in natural or artificial systems. Engineering goal: –specify methods for design of useful, intelligent artifacts. Psychological goal: –understanding/modeling people –cognitive science (not this course)

Goals of This Course Introduce key methods & techniques from AI –searching, –reasoning and decision making (logical and probabilistic) –learning language understanding, –... Understand applicability and limitations of these methods

Goals of This Course Our approach: –Characterize Environments –Identify agent that is most effective for each environment Study increasingly complicated agent architectures requiring –increasingly sophisticated representations, –increasingly powerful reasoning strategies

Intelligent Agents Definition: An Intelligent Agent perceives its environment via sensors and acts rationally upon that environment with its actuators. Hence, an agent gets percepts one at a time, and maps this percept sequence to actions. Properties –Autonomous –Interacts with other agents plus the environment –Adaptive to the environment –Pro-active (goal-directed)

Applications of Agents Autonomous delivery/cleaning robot –roams around home/office environment, delivering coffee, parcels,... vacuuming, dusting,... Diagnostic assistant helps a human troubleshoot problems and suggest repairs or treatments. –E.g., electrical problems, medical diagnosis. Infobot searches for information on computer system or network. Autonomous Space Probes...

Task Environments: PEAS P erformance Measure –Criterion of success Environment Actuators( เคลื่อนไหว ) –Mechanisms for the agent to affect the environment S ensors –Channels for the agent to perceive the environment

Example: Taxi Driving P erformance Measure –Safe, fast, legal, comfortable trip, maximize profit Environment –Roads, other traffic, pedestrians, customers Actuators –Steering, accelerator, break, signal, horn, … S ensors –Cameras, sonar, speedometer, GPS, …

Types of Environments Fully observable (accessible) or not Episodic( ตอน ) vs. sequential( ลำดับ ) Static vs. dynamic Discrete vs. continuous Single agent vs. multi-agent –competitive vs. cooperative

Agent Function and Program Agent specified by agent function –mapping percept sequences to actions –Aim: Concisely implement “rational agent function” Agent program –Input: a single percept-vector –Process: (keeps/updates internal state) –Output: returns action

Skeleton Agent Program function SkeletonAgent(percept) returns action static: memory, [agent's memory of the world] memory  UpdateMemory(memory,percept) action  ChooseBestAction(memory) memory  UpdateMemory(memory, action) return action

Types of Agents Simple reflex agents –Actions are determined by sensory input only Model-based reflex agents –Has internal states Goal-based agents –Action may be driven by a goal Utility-based agents –Maximizes a utility function

Simple Reflex Agent

Example A LEGO MindStorm TM program: if (isDark(leftLightSensor)) turnLeft() turnLeft() else if (isDark(rightLightSensor)) turnRight() else goStraight() What’s the agent function?

Model-Based Agent

Goal-based Agent

Utility-based Agent

Summary Intelligent Agent PEAS Types of Agents