A.I. – Cognitive Computing A personal view

Prof. Dr. Franz Wotawa Institute for Software Technology

Technische Universität Graz wotawa@ist.tugraz.at!

The origins of AI

•  Maybe the desire to

– Explain intelligence

– Build intelligent creatures

The Golem

Rabbi Judah Loew ben Bezalel Ca. 1580; Prague

The Turk

Wolfgang von Kempelen 1769

Frankenstein

Mary Shelley 1818

And most recently

Star Wars George Lucas 1977

I, Robot After a story by Isaac Asimov 2004

And many others...

RoboCup & RoboCup Junior

•  Since 1997

SO, WHAT CHARACTERIZES ARTIFICIAL INTELLIGENCE?

„... the science and engineering of making intelligent machines ...“

John McCarthy (1927-2011)

„Cognitive science is the interdisciplinary scientific study of the mind and its processes.“

From Wikipedia (April 2013)

What is intelligence? •  „A very general mental

capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. ....“

(from Mainstream Science on Intelligence" (1994), an editorial statement by fifty-two researchers)

11  

Research questions •  Is it possible to create intelligent beings?

•  What is intelligence?

•  What are the foundations behind intelligence and its representation?

•  How to construct smart / intelligent systems?

Main tasks

•  Perception

•  Knowledge representation

•  (Logical) Reasoning, problem solving

•  Learning

... and there are many problems to be solved ...

•  Planning, scheduling

•  Diagnosis

•  Configuration

•  …

LET‘S FOCUS ON DIAGNOSIS

Coming home at night

  "Turning on the light

    "But there is no light!

    "

The bulb is broken!     "

Let‘s try another one!

    "

But again no light! A fuse might be the reason!!

    "

How to diagnose (I) ? •  If there is no light:

“In 93 % of all cases the bulb is broken.”

•  Use probabilities to diagnose •  Diagnosis based on frequencies observed

over time

•  Also explains, the second case (the fuse is broken)!

How to diagnose (II) ?

•  Use models for diagnosis!

•  Cause-effect modeling

“IF turn_on_switch THEN light”

Explains effect light in case of turning on a switch!

Diagnosis based on models

•  What is missing?

•  There is implicit knowledge there!

•  “IF turn_on_switch THEN light”

AND  bulb_ok  

The role of observations •  Model:

IF turn_on_switch AND bulb_ok THEN light

turn_on AND bulb_ok

•  Observation:

NO light

•  There is a contradiction, which has to be resolved!

Taken it all together •  We have a model

– describing the correct behavior of a system, and

– making this explicit! (bulb_ok) •  We have observations that are in

contradiction with the model

•  Develop algorithm that searches for correctness assumptions that have to be removed!

Model-based diagnosis

Model  

Observa0ons  

System  

bulb_ok  fuse_ok  …  

Diagnosis of robots

Movie by Brandstötter, Steinbauer 2007 M. Hofbaur, J. Köb, G. Steinbauer, and F. Wotawa, Improving robustness of mobile robots using model-based reasoning, Journal of intelligent & robotic systems, vol. 48, no. 1, pp. 37–54, 2007.

1.  g1 = a1 && b1; 2.  p1 = !((a1 && b1) || (!a1 && !b1)); 3.  g2 = a2 && b2; 4.  p2 = !((a2 && b2) || (!a2 && b2)); 5.  g3 = a3 && b3; 6.  p3 = !((a3 && b3) || (!a3 && !b3)); 7.  g4 = a4 && b4; 8.  p4 = !((a4 && b4) || (!a4 && !b4)); 9.  z1 = !((c1 && p1) || (!c1 && !p1)); 10.  s11 = p1 && c1; 11.  s12 = g1 || s11; 12.  z2 = !((s12 && p2)||(!s12 && !p2)); 13.  s21 = c1 && p1 && p2; 14.  s22 = g1 && p2; 15.  s23 = (s21 || s22) || g2; 16.  z3 = !((s23 && p3) || (!s23 && !p3)); 17.  s31 = c1 && p1 && p2 && p3; 18.  s32 = g1 && p2 && p3; 19.  s33 = g2 && p3; 20.  s34 = ((s31 || s32) || s33) || g3; 21.  z4 = !((s34 && p4) || (!s34 && !p4)); 22.  s41 = c1 && p1 && p2 && p3 && p4; 23.  s42 = g1 && p2 && p3 && p4; 24.  s43 = g2 && p3 & p4; 25.  s44 = g3 && p4; 26.  cn = (((g4 || s41) || s42) || s43 ) ||

s44;

a1 = true a2 = false a3 = true a4 = false b1 = true b2 = false b3 = false b4 = false c1 = false z1 = false z2 = true z3 = true z4 = false cn = false

SOME EXAMPLES FROM RESEARCH AND INDUSTRY

Applications of AI Technology

Computational power success stories…

•  Deep Blue against Garry Kasparov (Chess, 1996, 1997)

•  Watson plays Jeopardy (2011)

The NELL Project

Tom  Mitchell;  see  http://rtw.ml.cmu.edu/rtw/!

Diagnosis of Waste Water Treatment Plants

Recommender Systems •  Austrian Company ConfigWorks •  For Banks, investment companies,

insurance companies, etc.

Deep Space 1

„Remote Agent (remote intelligent self-repair software)(RAX), developed at NASA Ames Research Center and JPL, was the first artificial intelligence control system to control a spacecraft without human supervision. Remote Agent successfully demonstrated the ability to plan onboard activities and correctly diagnose and respond to simulated faults in spacecraft components.“ (Wikipedia)

Robots

The future of AI technology

•  Hard to predict, but:

– More and more AI in today’s products • Cars, computers, mobile phones, …

– Robotics of increasing importance – Big data needs AI

THANK YOU FOR YOUR ATTENTION!