Presentation on theme: "Fundamentals of AI Introduction. COSC 159 - Fundamentals of AI2 Overview Syllabus Grading Topics What is AI? Four competing views Agents Course Goals."— Presentation transcript:
Fundamentals of AI Introduction
COSC Fundamentals of AI2 Overview Syllabus Grading Topics What is AI? Four competing views Agents Course Goals Summary
COSC Fundamentals of AI3 Syllabus Instructor information Prerequisites Programming Languages Textbook Russell and Norvig, AIMA, 2nd Edition Attendance
COSC Fundamentals of AI4 Grading Qualities of good work Communication Correctness Validation Comparison Efficiency Your work will be graded on all aspects
COSC Fundamentals of AI5 Topics Covered Definitions of AI Agents Problem representation and solving Searching, heuristics, optimization Knowledge representation and reasoning Logic Planning problems Uncertainty Learning More topics if we have time
COSC Fundamentals of AI6 What is AI? Understand and build intelligent entities Artificial refers to building entities What is intelligence? Understand and build an entity emulating a human? Understand and build an entity that is rational?
COSC Fundamentals of AI7 Rationality An ideal concept of intelligence Doing the right thing given available information How do we define the right thing? Suppose put your hand down on a hot stove. What is the rational response? Rationality does not always mean doing the best possible thing
COSC Fundamentals of AI8
9 Rationality Given the situation, was the boss’ action irrational? What would make the boss’ action irrational?
COSC Fundamentals of AI10 Competing Views of AI Many definitions that can be classified as follows (Russell and Norvig, 2003) Like HumansRationally Thinking Acting
COSC Fundamentals of AI11 Acting Humanly Turing test (1950)
COSC Fundamentals of AI12 Acting Humanly Goal: Make computers/entities act like humans Natural language processing Knowledge representation Automated reasoning Machine learning It is not important how the actions are chosen, as long as results in behavior indistinguishable from a human
COSC Fundamentals of AI13 Thinking Humanly Understand cognition Defined as the mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment. Simulate cognition on computers Cognitive science Experimental investigation of humans and animals The how is important
COSC Fundamentals of AI14 Thinking Rationally Attempt to codify “right thinking” Aristotle’s syllogisms (reasonings or patterns of argument) Logical approach Formal methods of representing knowledge Formal methods of reasoning Again, how a conclusion is reached is important
COSC Fundamentals of AI15 Acting Rationally Entities that do the right thing The how isn’t necessarily important Couple rational thinking with other methods What if there is no provably correct action? Consider the hot stove again Did the action require rational thought? Are reflex actions intelligent?
COSC Fundamentals of AI16 Applications Autonomous planning and scheduling NASA’s Remote Agent Game playing IBM’s Deep Blue Autonomous control ALVINN, drove 98% of the time across the country Diagnosis Medical diagnosis Pattern recognition Data mining and bioinformatics
COSC Fundamentals of AI17 Characteristics of AI Problems Frequently hard NP-hard, which implies there is no known efficient general solution Frequently complex Messy data, such as images, pressure, locations, natural language, etc. Frequently imprecise Uncertain situations Autonomy Cannot require human intervention, must adapt
COSC Fundamentals of AI18 Agents An agent is something that acts In this class, we will build software agents Agents that act rationally How are agents different from other programs? Autonomous Perceptive Persistent Adaptable Assume the goals of other agents
COSC Fundamentals of AI20 Definitions Percept sequence History of everything agent has perceive Agent function Map from percept sequence to action Agent program Implementation of agent function
COSC Fundamentals of AI21 Example Consider a world that has a starving monkey and a banana. Whenever the monkey is in the same location as the banana, the monkey will eat it. After eating the banana, the monkey falls asleep. We would like to build a simulation for the environment with a software agent representing the monkey. Consider a world with two locations.
COSC Fundamentals of AI22 Example U D
COSC Fundamentals of AI23 Example Assumptions Monkey can see the bananas and knows its location Defines percepts: (Location, Contents) Actions Up, down, eat, sleep
COSC Fundamentals of AI24 Example Agent function should move monkey to the bananas, eat the bananas, then sleep One possible agent program is to create a table mapping a percept sequence to appropriate action Table-driven agent
COSC Fundamentals of AI26 Questions to ponder Is a table driven agent a good way to implement rational behavior? Are all sequences of percepts possible in the environment? What if the monkey didn’t know its location, could you still devise a solution to the problem? How would the percepts change?
COSC Fundamentals of AI27 Measuring Rational Behavior What does it mean for an agent to do the right thing? The right action is the one causing the agent to be most successful. A performance measure embodies the criterion for an agent’s success.
COSC Fundamentals of AI28 Performance Measures Simple performance measure for monkey and bananas The monkey has eaten and fallen asleep. Suppose you have two monkeys, one that sleeps right after eating and one that wanders around and then falls asleep. Which one is better? Why?
COSC Fundamentals of AI29 Performance Measures Consider more complex environments What performance measure is appropriate for the economy? What about for stocks? How about medical diagnoses? What about driving a car? Performance measures are not easy to determine, but you must design one for each environment
COSC Fundamentals of AI30 Rationality Rational behavior at any given time depends on four things Performance measure Agent’s prior knowledge Actions agent can perform Agent’s percept sequence
COSC Fundamentals of AI31 Course Goals Understand and build intelligent entities Rational agents Formulate search problems Solve using uninformed and informed algorithms Represent and reason about knowledge Logic Formulate and solve planning problems STRIPS, partial order planners
COSC Fundamentals of AI32 Course Goals (cont.) Reason in uncertain situations Probability, Bayesian networks Introduce machine learning Inductive learning, decision trees
COSC Fundamentals of AI33 For Next Time Read through chapters 1 and 2. Think about how you would implement a simulation for the two location monkey and banana world.
COSC Fundamentals of AI34 Summary AI is the study and implementation of intelligent entities Several perspectives on AI We will take the rational action perspective The agent framework provides a unifying approach to AI Applications of AI are widespread and complex