IWKS 2300

Fall 2019

Artificial Intelligence and

Robotics

John K. Bennett

First, a little clean up from last class…

What we need to know:

1.Vfwd of LED

2.High (‘1’) voltage level of Ardunio “high”

3.Max and working high current of Arduino output

Let’s take these each in turn:

R What is the correct value of R?

A little clean up from last class…What we need to know:

1.Vfwd of LED

Vfwd (white LED at 10 ma) = 3.2V

A little clean up from last class…What we need to know:

2.High (‘1’) voltage level of Ardunio “high

V’1’ (10 ma at 25 deg. C) = 4.75V

A little clean up from last class…What we need to know:

3.Max and working high current of Arduino output

Imax = 40 ma

VoutH (5V VCC) = 4.2V

Split the difference and

make it 4.5V

A little clean up from last class…Now compute R:

R = V / I = (4.5 – 3.2) / .010ma

= 1.3V / .010

= 130 ohms (this is a min value; other color LEDs

have much lower Vfwd, e.g. red LED has a Vfwd of

1.3V: (4.5 – 1.3) / .010 = 320 ohm.

A white LED with 330 ohm R has a Ifwd of 4ma

So 270 ohm or 330 ohm gives us a safe

cushion for any LED, e.g.:

Vfwd (IR LED at 10 ma) = .9V

R = (4.5 – 0.9) / .010 = 360 ohm;

with 330 ohm R we get 11 ma (no problem)

with 130 ohm R we get 27 ma (not good!)

Now back to our regularly

scheduled programming…

What is Intelligence?

• Examples of intelligent

processes:

• Deduction

• Induction and learning

• Making rational decisions

• Intelligent embodied agents can:

• Perceive the world around them

• Act in the world

Examples of “Natural”

Intelligence

Viruses

• not considered by most biologists to be alive

• do not reproduce on their own, but make

bacteria produce copies of themselves

• no learning in individual viruses; only

“evolutionary learning”

Bacteria

• are considered by most biologists to be alive

• reproduce by dividing into two organisms

• capable of some simple learning that allows

them to move toward favorable environments

Insects

• capable of learning and memorizing

• primitive social interactions (trail of pheromones)

• simple visual and olfactory perception (compound eyes)

• coordinated movements of legs to master different types of terrains

Reptiles

• excellent vision and eye movement

• able to learn certain behaviors

• excellent hunting abilities

• very primitive social interactions

Higher Mammals

• powerful senses

• produce sounds but not language

• complex social interactions

• probably have consciousness, and in

possession of basic feelings that are related

to our own

Non-Human Primates

• human-like genetic makeup and behaviors

• produce different sounds with different meanings

• capable of learning symbolic “language”

• probably have consciousness, are self-aware and

possess basic feelings at the level of a human child

Humans

• complex social behaviors

• able to learn high-level syntactic languages

• extremely long phase of learning (upbringing)

• consciousness, self-awareness, abstract thinking

• awareness of past and future, planning capability

Hardware

1011 neurons

1014 synapses

cycle time: 10-3 sec

Hardware

1011 neurons

1014 synapses

cycle time: 10-3 sec

1010 transistors

1012 bits of RAM

cycle time: 10-9 sec

The Future

•In near future computers will have

• As many processing elements as our

brain,

• far fewer interconnections, and

• and much faster updates

•Fundamentally different hardware

• Likely requires fundamentally different

algorithms!

• Very much an open question.

What is Artificial

Intelligence?

History of AI

• Prehistory: Greek mythology: Talos, man of bronze, created by Hephaestus; Pygmalion brings Galatea (aka Liza Doolittle) to life.

History of AI

• Pre-20th Century: Talking heads were described, and attempts to build them were made between the 13th and 18th century, most conspicuously by Albertus Magnus and Sir Francis Bacon, alchemists. (Alchemists were also interested in creating a “homonculus,” or perfect miniature of a human.

History of AI

• Term robot was first introduced by

Czech playwright Karel Capek in the

1921 play Rossum’s Universal

Robots.

• The play featured machines created

to simulate human beings.

• Robota is the Czech word for “work”

or “forced workers”/“slaves”.

AI History Summary

• 50’s: Early AI programs, including Samuel's checkers

program, Newell & Simon's Logic Theorist, Gelernter's

Geometry Engine

• 60’s: Search and games, formal logic and theorem proving

• 70’s: Robotics, perception, knowledge representation,

expert systems

• 80’s: More expert systems, AI becomes an industry

• 90’s: Rational agents, probabilistic reasoning, machine

learning

• 00’s: Systems integrating many AI methods, machine

learning, reasoning under uncertainty

• 10’s: reasoning (machine learning) using massive amounts

of data

Definitions of AI vary

The exciting new effort to make computers think … machines with minds, in the full and literal sense” (Haugeland 1985)

“The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil, 1990)

“The study of mental faculties through the use of computational models” (Charniak et al. 1985)

A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes” (Schalkol, 1990)

What is AI?

Systems that think like humans

Systems that think rationally

Systems that act like humans

Systems that act rationally

Artificial Intelligence is

the study of:

1) Systems Acting like Humans

• Turing test: test for intelligent behaviour• Interrogator writes questions and receives

answers

• System providing the answers passes the test if interrogator cannot tell whether the answers come from a person or not

• Necessary components of such a system form major AI sub-disciplines:• Natural language, knowledge

representation, automated reasoning, machine learning

Acting Humanly: The Turing Test

The Turing Test

Problems:

1) Turing test is not reproducible, or amenable to mathematic analysis.

2) What about physical interaction with interrogator and environment?

What would a computer need to

pass the Turing test?• Natural language processing: to

communicate with examiner.

• Knowledge representation: to store and

retrieve information provided before or during

interrogation.

• Automated reasoning: to use the stored

information to answer questions and to draw

new conclusions.

• Machine learning: to adapt to new

circumstances and to detect and extrapolate

patterns.

2) Systems Thinking like Humans

• Formulate a theory of mind/brain

• Express the theory in a computer

program

• Two Approaches

• Cognitive Science and Psychology

(testing/ predicting responses of

human subjects)

• Cognitive Neuroscience (observing

neurological data)

Thinking Humanly: Cognitive Science

• 1960’s “Cognitive Revolution”: information-processing

psychology replaced behaviorism

• Cognitive science brings together theories and

experimental evidence to model internal activities of

the brain

• What level of abstraction? “Knowledge” or

“Circuits”?

• How to validate models?

• Predicting/testing behavior of human subjects (top-down)

• Direct identification from neurological data (bottom-up)

• Building computer/machine simulated models and

reproduce results (simulation)

3) Systems Thinking Rationally

• “Rational” = ideal intelligence

(contrast with human intelligence)

• Rational thinking governed by precise “laws of thought”

• syllogisms

• notation and logic

• Systems (in theory) can solve problems using such laws

Thinking Rationally: Laws of Thought

• Aristotle (~ 450 B.C.) attempted to

codify “right thinking”

What are correct arguments/thought

processes?

• E.g., “Socrates is a man, all men are

mortal; therefore Socrates is mortal”

• Several Greek schools developed

various forms of logic:notation plus rules of derivation for thoughts.

(led to propositional and predicate logic)

Thinking Rationally: Laws of Thought

Problems:

1)Uncertainty: Not all facts are certain

(e.g., the flight might be delayed).

2)Resource limitations:

• Not enough time to compute/process

• Insufficient memory/disk/etc.

• Etc.

4) Systems Acting Rationally

• Building systems that carry out

actions to achieve the best outcome

• Rational behaviour

• May or may not involve rational

thinking

• i.e., consider reflex action

This is the most commonly adopted

definition

Rational Behavior

Getting computers to do the right

thing based on their circumstances

and what they know.

• No presuppositions about how they should

be designed to do the right thing

• not limited to how people do it

• Evaluation is based on performance, not on

how the task is performed

How to Achieve AI?• How is AI research done?

• AI research has both theoretical and experimentalsides. The experimental side has both basic and applied aspects.

• There are two main lines of research:• One is biological, based on the idea that since humans are

intelligent, AI should study humans and imitate their psychology or physiology.

• The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of goals.

• The two approaches interact to some extent, and both should eventually succeed. “It is a race, but both racers seem to be walking.” (John McCarthy)

Applied Areas of AI

• Game play

• Speech and language processing

• Expert reasoning

• Planning and scheduling

• Vision

• Robotics

• Autonomous Vehicles

• Machine Learning (lots of theory here too)

Foundations of AI

• Philosophy: logic, mind, knowledge

• Mathematics: proof, computability,

probability

• Economics: maximizing payoffs

• Neuroscience: brain and neurons

• Psychology: thought, perception, action

• Control Theory: stable feedback systems

• Linguistics: knowledge representation,

syntax

• Data Science: knowledge synthesis

Main Areas of AI Knowledge

representation (including formal logic)

Search, especially heuristic search (puzzles, games)

Planning

Reasoning under uncertainty, including probabilistic reasoning

Learning

Agent architectures

Robotics and perception

Natural language processing

Search

Knowledgerep.

Planning

Reasoning

Learning

Agent

Robotics

Perception

Naturallanguage

... ExpertSystems

Constraintsatisfaction

AI State of the Art

• Which of the following been achieved?• World-class chess playing

• Playing table tennis

• Cross-country driving

• Solving mathematical problems

• Discover and prove mathematical theories

• Engage in a meaningful conversation

• Understand spoken language

• Observe and understand human emotions

• Express emotions

• …

AI State of the Art

• Which of the following been achieved?• World-class chess playing

• Playing table tennis

• Cross-country driving

• Solving mathematical problems

• Discover and prove mathematical theories

• Engage in a meaningful conversation

• Understand spoken language

• Observe and understand human emotions

• Express emotions

• Have emotions

State of the Art

“I could feel – I could smell – a new kind of intelligence across the table”

- Gary Kasparov

State of the Art

“Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn’t really fly because it doesn’t flap its wings.”

– Drew McDermott

Mathematical Calculation

“The Wolfram Language has state-of-the-art capabilities for

the construction, training and deployment of neural network

machine learning systems. Many standard layer types are

available and are assembled symbolically into a network,

which can then immediately be trained and deployed on

available CPUs and GPUs.”

Speech Recognition

Speech Recognition

Repair Scheduling

Autonomous Vehicles

Limits of AI Today

• Today’s successful AI systems

• operate in well-defined domains

• employ narrow, specialize knowledge

• Common-sense Knowledge

• needed in complex, open-ended worlds

• Your kitchen vs. GM factory floor

• understand unconstrained natural language

How do we understand?

• John gave Pete a book.

• John gave Pete a hard time.

• John gave Pete a black eye.

• John gave in.

• John gave up.

• John gave it a go.

• John gave a good accounting of himself.

• John’s legs gave out.

• Give it up for John.

• It is 300 miles, give or take a few.

Can Machines Act/Think Intelligently?

Yes, if intelligence is narrowly defined as

information processing

AI has made impressive achievements

showing that tasks initially assumed to

require intelligence can be automated

Probably not, if intelligence is not

separated from the rest of “being

human”

Major Issues In AI

• How to represent knowledge about the world?

• How to react to new perceived events?

• How to integrate new percepts to past experience?

• How to understand the user?

• How to optimize balance between user goals &

environment constraints?

• How to use reasoning to decide on the best course of

action?

• How to communicate back with the user?

• How to plan ahead?

• How to learn from experience?

Some Big Open Questions

• AI (especially, the “rational agent” approach) assumes that intelligent behaviors are only based on information processing? Is this a valid assumption?

• If yes, can the human brain machinery solve problems that are inherently intractable for computers?

• In a human being, where is the interface between “intelligence” and the rest of “human nature”, e.g.:• How does intelligence relate to emotions felt?

• What does it mean for a human to “feel” that he/she understands something?

• Is this interface critical to intelligence? Can there exist a general theory of intelligence independent of human beings?

Asimov’s I, Robot

In the movie I, Robot, the most impressive feature of the robots is not their ability to solve complex problems, but how they blend human-like reasoning with other key aspects of human beings (especially, self-consciousness, fear of dying, distinction between right and wrong).

AI – A CommentDespite all these achievements, one of the major

philosophers of Cognitive Science wrote:

“… the failure of artificial intelligence to produce

successful simulation of routine commonsense

cognitive competences is notorious, not to say

scandalous. We still don't have the fabled machine

that can make breakfast without burning down the

house; or the one that can translate everyday English

into everyday Italian, or the one that can summarize

texts..”

(Jerry Fodor, The Mind doesn’t Work that Way, 2000)

AI in Business

“Our current AI state is that of a toddler. It can

understand what it sees, what it hears, and then tell you

what it is seeing. This is just the early stage for AI, but

for businesses to really gain value from it, the next step

will be the ability to make more connections and

reasoning about the connections between objects. We

are progressing down a path from going from a toddler

to a slightly more capable learning machine.” Todd Anglin, VP Product Strategy, Progress Software, Nov. 28,

2017

What is a Robot?

Narrow Definition

• “A robot is a reprogrammable

multifunctional manipulator designed to

move material, parts, tools, or

specialized devices through variable

programmed motions for the

performance of a variety of tasks.”

From the Robot Institute of America,1980.

Broader Definition

“A mobile device that

operates with some

degree of autonomy,

usually under computer

control.”

Other Definitions

• An efficient, insensitive person.

• A person who works mechanically

without original thought,

especially one who responds

automatically to the commands of

others.

• A machine that looks like a

human being.

Two Main Design Goals

• Mobility

• Main goal is transport

• Manipulation

• Main goal is to perform an

action on the environment.

Robotics Applications

• Industrial

• Dominant from commercial standpoint

• Manufacturing

• Service

• Everything not industrial

• Telerobots (master/slave systems)

• Virtual Reality Interfaces

Mobile Robot Examples

• Search and Rescue

• Remote-controlled

• Robust, Tethered

Center for Robot-Assisted Search and Rescue

Mobile Robot Examples

• Guides (museum,

tourist attractions,

etc.)Rhino (CMU/Bonn ICS)

Museum Tour-Guide Robots

Rhino, 1997 Minerva, 1998

“How Intelligent Is Minerva?”

• Amoeba?

• Fish?

• Dog?

• Monkey?

• Human?

“How Intelligent Is Minerva?”

fish dog monkey humanamoeba

5.7%

29.5%

25.4%

36.9%

2.5%

“Is Minerva Alive?"

undecided noyes

3.2%

27.0%

69.8%

Mobile Robot Examples

• Walking & running robots

(one or more legs)

• Purpose:

• Handle uneven terrain

• Help us understand

biological locomotionSprawlita (Stanford)

Mobile Robot Examples

• Hopping robots

use accurate

dynamic models

of the system.

• Specialized

controllers

stabilize the

system.

3D Biped (MIT)

Mobile Robot Examples

• Soccer-playing robots

• Cooperating Agents

CMU RoboCup Entry

LegoRobots Game Board

“Bob”

“Jaundice 5”

“Le Saboteur”

“Scooby Doo”

“Psycha...”

RoboCup Challenge

Design a team of robots

that can play soccer!

Manipulator Examples

• Medical robots

(tele-operated)

Manipulator Examples

• Space shuttle arm

(tele-operated)

Manipulator Examples

• Painting robots

• Interesting issues in

coverage, path

planning

Manipulator Examples

• Industrial robots (tele-

operated &

autonomous)

Manipulator Examples

• Baking

Robots!?!

Block Grabber

Sensor Details

Are the following robots?

• Automobile?

• Washing Machine?

• Treadmill?

• Cockroach with implanted control

chip?

• A haptic device?

• A battlebot?

2005 DARPA Challenge

DARPA Grand Challenge

• Race of autonomous vehicles across

California desert

• Vehicles are given a route as series of GPS

waypoints

• But they must intelligently avoid obstacles

and stay on the road

• About 130 miles of dirt roads, off-road,

normal roads, bridges, tunnels, etc.

• Must complete in less than 10 hours

2007 ChallengeThe DARPA Urban Challenge featured autonomous

ground vehicles executing simulated military supply

missions safely and effectively in a mock urban area. Safe

operation in traffic is essential to U.S. military plans to use

autonomous ground vehicles to conduct important

missions.

DARPA awarded prizes for the top three autonomous

ground vehicles that compete in the final event where they

must safely complete a 60-mile urban area course in

fewer than six hours. First prize was $2 million, second

prize was $500,000 and third prize was $250,000. To

succeed, vehicles had to autonomously obey traffic laws

while merging into moving traffic, navigating traffic circles,

negotiating busy intersections and avoiding obstacles.

Honda Asimo Humanoid Robot

Walk

Turn

Stairshttp://world.honda.com/robot/

Sony AIBO

http://www.aibo.com

2009-2011 Urban Challenge• Vehicle must be built upon a full-size stock chassis.

• Vehicle must be capable of loading a mission description

file (MDF) via a standard USB 2.0 flash drive. The vehicle will

have 5 minutes to process a MDF before attempting the

course. The MDF will include information such as accessible

roads, all areas in which the vehicle may travel, stop sign

locations, nominal lane width, maximum and minimum speed

limits, lane markings, and parking spot locations. The MDF

has no implied start or end points. Road blockages will not be

indicated in the MDF.

• To complete the requirements for the Urban Challenge,

each vehicle will complete multiple missions over a defined

route. A mission is a series of checkpoint locations that must

be passed over sequentially by the vehicle. The path between

checkpoints is not specified.

DARPA Urban Challenge

2012 DARPA Grand ChallengeIn the DARPA Robotics Challenge, robots competed with each other

performing disaster response operations in representative scenarios that

could include the following sequence of events:

• Drive a utility vehicle at the site.

• Travel dismounted across rubble.

• Remove debris blocking an entryway.

• Open a door and enter a building.

• Climb an industrial ladder

and traverse an industrial

walkway.

• Use a tool to break through

a concrete panel.

• Locate and close a valve

near a leaking pipe.

• Replace a component

such as a cooling pump.

2015 DARPA Grand Challenge

The DRC Finals competition challenged participating robotics teams and

their robots to complete a difficult course of eight tasks relevant to disaster

response, among them driving alone, walking through rubble, tripping circuit

breakers, turning valves and climbing stairs.

DARPA Subterranean Challenge

(2017-Present)

Current technologies fail to provide persistent situational awareness of the

diverse subterranean operating environments, including tunnels, urban

underground, and cave networks. DARPA encourages participation from

multidisciplinary teams from around the world to address the autonomy,

perception, networking and mobility technologies necessary to map

subsurface networks in unpredictable conditions. To attract a broader range

of participants, the SubT Challenge includes both a physical Systems

competition, as well as a software-only Virtual competition.

Ethics and Societal Impact

• Should we (or can we) make robots

that are autonomously intelligent?

• Should we create autonomously

self-replicating robots?

• How should humans interact with

robots?

• Should humans be replaced by

robots for certain tasks?

Asimov’s 3 (4) Laws of Robotics

• From a 1940’s short story (“I Robot”)

1. Robots must never harm human beings.

2. Robots must follow instructions from humans without violating Rule 1.

3. Robots must protect themselves without violating the other rules.

• Asimov’s robots added Rule 0: Robots must act to prevent harm to humans

(A Few) Issues in Robot Design

• Kinematics

• Dynamics

• Design

• Controls

• Special Areas of Focus:

• Path Planning

• Computer Vision

• Haptics

• Medical Robotics

• Artificial Intelligence