a really good book on AI (used on many universities)
four different approaches:
Thinking humanly - the cognitive science approach. AI models the actual mental processes of humans (how brains work). Closely tied to psychology and neuroscience.
Thinking rationally - the “laws of thought” approach. Uses formal logic to represent and reason about the world correctly. Rooted in Aristotle’s syllogisms.
Acting humanly - the Turing Test approach. A machine is intelligent if its behavior is indistinguishable from a human’s. Focus on observable output, not the internal process.
Acting rationally - the rational agent approach, which the book itself adopts. An agent acts to achieve the best expected outcome given its goals and available information. This is the dominant paradigm in modern AI.
Turing test
by Alan Turing, he founded the foundations of Turing machines (and laws of computability), he also helped to resolve the Enigma cypher code
A.L.I.C.E - a simple, pattern matching algorithm, that has performed really well of simulating a human behavior
this test basically says: “if it acts intelligently, it is intelligent”
A Chinese room argument
a program could look smart even though it’s just reading information without understanding them properly (a guy in a room full of Chinese books just reads from them without actually undestanding, what do they mean)
it was a philosophical argument - distinguishing syntax and semantics
it contradicts the Turing test, if it behaves intelligently, it could just be a sophisticated work with letters and syntax that is far from understanding the semantics of it
History
Shakey - a first autonomous robot
was able to get the environment around it, plan the task and execute it successfully
DARPA Grand Challenge - first competition of autonomous self-driving cars
the biggest problem is the explainability of the AI
if it is not explainable, we cannot debug it, see, why it decided in the way it has decided
the autonomous cars are not that explainable, and that’s a problem
the chatbots etc. are also not-explainable
Deep Blue vs. Garry Gasparov
AlphaGo
Neuro-symbolic AI
neuro part: machine learning (gaining knowledge from human experience etc.)
symbolic part: the A.I. part: no knowledge, but strong reasoning and explainability
the connection of those two parts would be best, but it’s still not developed to some good level
it’s an open challenge
Branches of AI
it ranges from computer vision, pattern recognition, planning/scheduling, machine learning, robotics, agent systems, text generation, autonomous robots/cars etc.
Tutorial 1
add Surynek to Gitlab whenever I want to check my homework and get points
if he leaves the project, it means that he has checked it
for next assignment, I will have to add him again
problem definitions:
2 jugs problem
fill the 5l to the full
pour 3l to the 3l, leaving 2l in the 5l one
empty the 3l one
pour the remaining 2l to the 3l one
fill the 5l up again
and pour the 1l to the 3l one
leaving 4 l in the 5l
how to move a horse around a chessboard to visit all the cells?
for any n
how to put queens onto a chessboard so they cannot endanger one another?
for any n
state space
for these kinds of problems (if we want to transfer them into graph problems):
we need to define a state (= vertex)
it’s kind of a snapshot of the environment (only the important facts)
here it would be an ordered pair (number of liters in first, number of liters in the second)
then we need to decide which type of graph it would be
(un)directed graph, here it would be directed graph