CMPUT 366 Intelligent Systems: Introduction to Artificial Intelligence Instructor: Prof. Jia You

Instruction Team Prof: Jia You Office hours: Tue, Thur: 11:00-10:50, or by appointment Phone: TAs: Gang Wu, Xiang Wan, Chonghai wang Home Page: Announcements Slides Assignments

Textbooks Required S Russell and P Norvig Artificial Intelligence: A Modern Approach, Prentice Hall, 2003 (2 nd Edition).

Evaluation 4 Assignments Solo! (see code of conducts) Total 4 late days allowed Paper/Pencil/Typed  Submit hard copy on due date before class, write legibly, or typed (better) Implementations (code)  Submit using ASTEP. The deadline is 11:59pm on the due date.  The implementations must run on the lab machines (in CSC 219) Final Exam 38% Closed book but allows notes on one 8X11 white paper.

Other Issues Prerequisites Programming skills (C++, Java) Elementary probability theory AI Seminar Friday noons, CSC333 Neat topics, great speakers, FREE PIZZA!

Course Overview Introduction: intelligent agent Search and constraint satisfaction Logical agent and planning Probabilistic reasoning Constraint Programming Others (time permitting)

What is Artificial Intelligence (AI)? Discipline that systematizes and automates intellectual tasks to create machines that: Act like humansAct rationally Think like humansThink rationally

Act Like Humans AI is the art of creating machines that perform functions that require intelligence when performed by humans Methodology: Take an intellectual task at which people are better and make a computer do it Prove a theorem Play chess Plan a surgical operation Diagnose a disease Navigate in a building

Turing Test Alan TuringAlan Turing, a mathematician who not only cracked the German code making machine, Enigma during the Second World War, but invented the concept of computers as we know them. Turing asserted that if you can fool a human into believing that he/she is receiving answers from another human when in fact it is a computer, this proves that computers are doing essentially what human brains do.

“Can machines think” -> “Can machines behave intelligently?” Operational test of intelligence: Imitation Game: Problem: Turing Test is not reproducible, constructive, or amenable to mathematical analysis.

Think Like Humans How the computer performs functions does matter Comparison of the traces of the reasoning steps Cognitive science  testable theories of the workings of the human mind But, do we want to duplicate human imperfections?

Think Rationally: Laws of Thought Normative (or prescriptive) rather than descriptive Aristotle: what are correct arguments/thought processes? Several Greek schools developed forms of logic: notation and rules of derivation for thoughts. Problems: Not all intelligent behavior is mediated by logical deliberation What is the purpose of thinking? What thoughts should I have?

Act Rationally Rational behavior: doing the right thing “The right thing”: that which is expected to maximize goal achievement, given the available information Limited resource, imperfect knowledge Rationality ≠ Omniscience, Rationality ≠ Clairvoyance, Rationality ≠ Successes Doesn't necessarily (but often) involve thinking Ignores the role of consciousness, emotions, fear of dying, … Doesn’t necessarily have anything to do with how humans solve the same problem.

AI History

Trends Since 90’s Relying less on logic and more on probability theory and statistics. More emphasis on objective performance evaluation. Intelligent Agents Computations: Constraints and constraint programming Accomplishments in Game playing: Deep blue, Chinook, … Space Probe Biological sequence analysis Consumer electronics ……

Notion of an Agent environment agent ? sensors actuators laser range finder sonars touch sensors Source: robotics.stanford.edu/~latombe/cs121/2003/home.htmrobotics.stanford.edu/~latombe/cs121/2003/home.htm

Notion of an Agent environment agent ? sensors actuators Locality of sensors/actuators Imperfect modeling Time/resource constraints Sequential interaction Multi-agent worlds Source: robotics.stanford.edu/~latombe/cs121/2003/home.htmrobotics.stanford.edu/~latombe/cs121/2003/home.htm

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 Source: robotics.stanford.edu/~latombe/cs121/2003/home.htmrobotics.stanford.edu/~latombe/cs121/2003/home.htm

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) Solving computationally hard problems Learning (covered in detail in CMPUT466) 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 acutators. 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 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, speedometer, GPS, …

Types of Environments Fully observable (accessible) or not Deterministic vs. stochastic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent competitive vs. cooperative

Example: Cleaning Agent

P erformance Measure ?? Environment ?? Actuators ?? S ensors ??

SurfBot Automated web surfing A SurfBot operates in the environment of the web. takes in high-level, perhaps informal, queries finds relevant information presents information in meaningful way

Performance Measure ?? Environment ?? Actuators ?? Sensors ??

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 (keeps/updates internal state) 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

Model-Based Agent

Goal-based Agent

Utility-based Agent

Summary What is AI? Rationality A bit of History Intelligent Agent PEAS Types of Agents