plan sztuczna inteligencja pojęcie sztucznej inteligencji w logistyce · sztuczna inteligencja w...
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Jerzy KORCZAKemail :[email protected]
http://www.korczak-leliwa.pl
Sztuczna inteligencja
w logistyce
Wrocław, 2019
Plan
Pojęcie Sztucznej Inteligencji
Kilka problemów algorytmicznych
Inteligencja obliczniowa– AG i sieci neuronowe
Platforma – SUMO
Projekt
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Pojęcie Sztucznej Inteligencji
Russell S.J., Norvig P., (2009) : Artificial Intelligence : A Modern Approach, Prentice Hall.
Winston H.P., Horn, B.K.P. (1992): Artificial Intelligence, Addison-Wesley, 3rd Ed.
Artificial Intelligence is … the study of the computations that make itpossilible to perceive, reason, and act [P.Winston]
- the engineering goal of AI is to solve real-world problems
- the scientific goal : models of KR, theory of reasoning,...
Intelligent systems: auto-adaptation
• ability to improve the quality of actions through gaining new experiences
• ability to adjust work parameters depending on the effects
• ability to solve tasks• ability to improve learning strategies
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Test of intelligence: is it possible?
A real-life Turing test: An interviewer (sitting in a separate room) asks a
series of questions that are randomly directed to either a computer or aperson. Based on the answers, the interviewer must distinguish which of
the two has answered the question. If the interviewer is not able to
distinguish between them, then the computer is intelligent.
Loebner Price ($100,000) … vs M. Minsky
Chess: “Deep Blue vs G.Kasparow 3.5 : 2.5”
- 10120 possible games
- 200 M anlayzed positions per second
- “opening book & extended book”
2016 – Deep Learning-based defeats a Go world champion
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Kilka problemów
Traveler Salesman Problem (TSP)
Given a number of cities and the costs of
travelling from any city to any other city,
what is the cheapest round-trip route that
visits each city exactly once and then
returns to the starting city?
William Rowan Hamilton
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Miary odległości – dystans euklidesowy
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Metryka Manhattan
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Odległość Czebyszewa
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S(x,y) = max|xi - yi|
Simple TSP
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21 Node 1 2 3 4
1 0 5 2 4
2 5 0 3 2
3 2 3 0 6
4 4 2 6 0
Cost matrix (distances)
Goal: find out a path with minimum cost
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2
6
3
3
2
Initially, T(1,{2,3,4}) = min of
{(1,2) + T(2, {3,4}) = 5 + 8 = 13{(1,3) + T(3, {2,4,}) =
{(1,4) + T(4, {2,3,}) =
Simple TSP
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21 Node 1 2 3 4
1 0 5 2 4
2 5 0 3 2
3 2 3 0 6
4 4 2 6 0
Cost matrix (distances)5
2
6
4
3
2
T(1,{2,3,4}) = min of
{(1,2) + T(2, {3,4}) = 5 + 8 = 13{(1,3) + T(3, {2,4}) = 2 + 5 = 7
{(1,4) + T(4, {2,3}) = 4 + 5 = 9
Simple TSP
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21 Node 1 2 3 4
1 0 5 2 4
2 5 0 3 2
3 2 3 0 6
4 4 2 6 0
Cost matrix (distances)
Solution:
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2
6
4
3
2
Minimum distance is 11 (path 1 3 2 4 1)
What about the time and space complexity?
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Brute Force search (exhaustive search)
… it consists systematically enumerating all possible
candidates for the solution and checking whether satisfies the
problem’s statement
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BranchBound
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… explores branches of this tree, which represent subsets
of the solution set. Before enumerating the candidate solutions of a branch, the branch is checked against upper
and lower estimated bounds on the optimal solution
Dijkstra's algorithm (Shortest Path First)
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… allows to calculate the
shortest path between one node and every
other node in the graph.
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Algorithm A* : 8-puzzleGoal: minimise the cost f(n)
f(n) = d(cost from start to n) + d(the cheapest cost from n to the goal) = g(n) + h(n)
Under-estimation h(n)
n h(n) <= h*(n) with h*(n) = real cost
1 4
2 5
7
3 6
8
2
4 8
3 5 6
1 7
2
4 8
3 5 6
1
7
8
2
4
3 5 6
1 7 2
4 8
3
5
6
1 7 2
4 8
3 5 6
1 7
1+5=6 1+6=7 1+4=5 1+5=6
2
4 8
3
5
6
1 7 2
4 8
3
5
6
1 7
2+3=5 2+5=7
SolutionStart
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Find the path from Wrocław to Warsaw
Easy!
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TSP with 8 cities using current IT solutions
Google maps application
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also easy! But what
about the path Gdańsk ->Warsaw?
Find a path from Wrocław to Warsaw and Gdańsk
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What happens if we add another cities to travel?
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Example with 8 cities:
{Szczecin, Gdańsk, Białystok, Poznań, Warszawa, Wrocław, Kraków, Lublin}
How many
paths possible?
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City 1 City 2 Distance Time Toll Route
Wrocław Gdańsk 450 5h45
Wrocław Kraków 330 4h15
Wrocław Lublin 470 5h30
Wrocław Poznań 170 2h30
Wrocław Szczecin 380 4h30
Wrocław Białystok 530 5h30
Wrocław Warszawa 350 3h30
Poznań Białystok 500 5h15
Poznań Gdansk 330 3h50
Poznań Kraków 460 5h00
Poznań Lublin 470 5h30
Poznań Szczecin 260 2h45
Poznań Warszawa 310 3h15
Szczecin Białystok 750 7h50
Try to solve the problem with the following data:
Complete the
table using
Google maps
Other approaches
Various branch-and-bound algorithms, which can be
used to process TSPs containing 40–60 cities.
Progressive improvement algorithms which use
techniques reminiscent of linear programming. Works well for up to 200 cities.
Implementations of branch-and-bound and problem-specific cut generation (branch-and-cut); this is the
method of choice for solving large instances. The current
record: 85,900 cities
Computational intelligence: GA, ant colony,…
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Heuristics
Heuristics ( hill climbing, local methods)
Attempt to maximize a target function f(x,y)
by application of an operator R that moves
from (xn-1
,yn-1
) to (xn,y
n) in a way that
f(xn,y
n) > f(x
n-1,y
n-1)
Two classes of operators:
1) gradient descent f
2) evaluation of f in environment
of (xn-1
,yn-1
)
Evolution-based methods
?
GA: Representation
Goal: represent the solution of the problem in a
computer-based form
Most important part of a GA
Two similar solutions should have similar
representations
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Evolution-based approach: search space
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Genetic algorithms
Genetic programming
Learning classifiers
Evolutionary strategies
… evolutionary algorithms are based on the
mechanics of natural selction and genetics.
Ch. DARWIN « On the Origin of Species by Means of Natural Selection », 1859
« surivival of the fittest »
Approaches in Artificial Evolution
Example of GA: Representation
List of cities to visit:
1) London 3) Vigo 5) Beijing 7) Tokyo
2) Venice 4) Singapore 6) Phoenix 8) Victoria
CityList1 (3 5 7 2 1 6 4 8)
CityList2 (2 5 7 6 8 1 3 4)
An “individual”
or “solution”
A “gene”
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GA: Initialization
Goal: generate a random population of individuals
First pool of solutions (“primordial soup”) given by
the GA
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Genetic operation: Two-point crossover
In two-point crossover, two routes are picked randomly from
the parent chromosomes. The cities in the two routes are swapped between the parents. The results in two children
carrying some genetic information from both parents.
Example:
Parents:
CityList1 (3 5 7 2 1 6 4 8)
CityList2 (2 5 7 6 8 1 3 4)Children
CityList3 ( 3 5 7 6 1 2 4 8)
CityList4 ( 2 5 7 2 8 6 3 4)
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Crossover points
GA: Initialization
London 3) Vigo 5) Beijing 7 ) Tokyo
2) Venice 4) Singapore 6) Phoenix 8) Victoria
CityList1 (3 5 7 2 1 6 4 8)
Many solutions of this
kind are created!!
CityList1 (3 5 7 2 1 6 4 8)CityList2 (2 5 4 1 8 6 7 3)...CityList100 (6 8 1 2 5 7 4 3)
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GA: Evaluation
Goal: gives a score (or fitness) to the solutions
Second most important part of a GA
Fitness = how good is a solution
In our application: length of the pathFitness(CityList1 ) = 71.537 km
Goal: MINIMIZE IT!!
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GA: Selection of parents Goal: select the parents to build the next generation
Based on elitism, roulette wheel, ...
Individual f(x) Fitness
(3 5 7 2 1 6 4 8) 71.537 km 0.16
(4 3 7 1 2 6 5 8) 30.532 km 0.21
(5 6 1 4 2 8 3 7) 95.262 km 0.13
(7 5 3 2 1 4 8 6) 155.650 km 0.06
(7 1 8 2 3 5 4 6) 28.508 km 0.21
(2 5 4 8 7 6 3 1) 19.417 km 0.23
Sum: 400 906 km 1
We select randomly
parents, lowest fitness will
be prefered
0.16%
0.21%
0.13%
0.06%
0.21%
0.23%
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GA: Crossover of parents Goal: exploit knowledge in both parents
Exchange genetic material from both parents
Typical on binary strings: one point crossover
On ordered list: OX1 operator
Parent 1 (3 5 7 2 1 6 4 8)
Parent 2 (1 6 7 4 8 3 5 2)
Child 1 (5 2 7 4 8 3 1 6)
Child 2 (4 8 7 2 1 6 3 5)
We exchange a part, then the
remaining is filled by omitting
the existing values
(3 5 7 2 1 6 4 8)
(1 6 7 4 8 3 5 2)
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GA: Mutation of parents Goal: explore new search space
Modify randomly one gene
Parent (3 5 7 2 1 6 4 8)
Child (3 5 6 2 1 7 4 8)
Mutations are random: we do
NOT care about results!!
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GA: Final solution
After many, many generations, we
finally found a good solution!!
Fitness(CityList1015 ) = 37.797 km
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Ant Colony Optimisation (ACS)
Large number of virtual ant agents
Virtual pheromone deposits on the edgeFind edges with max pheromone depos
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Alternate solutions are too slow or overly complicated
Need an exploratory tool to examine new approaches
Problem is similar to one that has already been
successfully solved by using a GA
Want to hybridize with an existing solution
Benefits of the GA technology meet key problem
requirements
When to use a GA?
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Traffic simulation
http://http://www.traffic-simulation.de/
ALVINN
Autonomous driving at 70 mph on a public highway
Camera image
30x32 pixelsas inputs
30 outputsfor steering 30x32 weights
into one out offour hiddenunit
4 hiddenunits
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Concept of neural network
….A neural network is an interconnected assembly of
simple processing elements, nodes, whose functionality is loosely based on the animal neuron. The processing
ability of the network is stored in the inter-unit connection
strengths, or weights, obtained by a process of adaptation
to, or learning from, a set of training patterns.” — Gurney
“... a neural network is a system composed of many
simple processing elements operating in parallel whose
function is determined by network structure, connection
strengths, and the processing performed at computing elements or nodes.” — DARPA Neural Network Study
(1988)
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Idea of neuron operation
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Deep Neural Network
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Tesla’s new AI – Autopilot software
Levels of autonomous driving capability:
1. One function is automated – Electronic Stability Control
2. Multiple functions working together are automated:
automatic emergency braking and lane keeping
3. Cars can drive themselves, but drives are needed to
intervene if necessary
4. Humans are’nt needed at all
5. Cars could go from point A to B with no human
intervention.
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Demo: https://www.techrepublic.com/article/autonomous-driving-levels-0-to-5-understanding-the-differences/
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Monitoring of
deliveries
Delivery
executed
Quantitative and
qualitative report
The driver prior
to any action transmits the
status of information on
the progress of work
The system can also
send a message to the customer about
the estimated time of delivery or possible
delays
Re
al-
tim
e info
rmation
Application
Transport coordinator
Client
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Application
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To see
• https://www.youtube.com/watch?v=tlThdr3O5Qo
• https://www.youtube.com/watch?v=rD9PGi8hHvY
• https://www.youtube.com/watch?v=vmw7w97a6LA
• https://www.youtube.com/watch?v=aqrttLPjv1E
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preceding processes
multimodal logistics-hub
here: airport
CC-Platform for
control & optimization ofmultimodal, fragmented
process chains
consequent processes
Far Perspective
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SUMO – Simulation of Urban MObility
SUMO is a free and open traffic simulation
suite, it allows modelling of intermodal traffic systems including road vehicles,
public transport and pedestrians.
Traffic simulations facilitate the evaluation of infrastructure changes as well as policy
changes before implementing them on
the road. Included with SUMO is a wealth of
supporting tools which handle tasks such
as route finding, visualization, network import and emission calculation. SUMO can be enhanced with custom models and provides various APIs
to remotely control the simulation.
dlr.de/ts/sumo
SUMO – features
• Microscopic simulation - vehicles, pedestrians and public
transport are modeled explicitly
• Online interaction – control the simulation with TraCI
• Simulation of multimodal traffic, e.g., vehicles, public
transport and pedestrians
• Time schedules of traffic lights can be imported or
generated automatically by SUMO
• No artificial limitations in network size and number of
simulated vehicles
• Supported import formats: OpenStreetMap, VISUM,
VISSIM, NavTeq
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Use Cases in Logistics
Intelligent Robotic Sorting
Autonomous guided vehicles (AGVs)
AI-Powered Visual Inspection
Computer Vision Inventory Management & Execution
Conversational Interfaces, voice agents
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Seeing, speaking & thinking logistics operations
Advanced Business IT-services, e.g.• Financial accounting, Business-Planning, Controlling
• ERP/ERM / CRM / SRM / PRM• E-Procurement, E-Commerce
Advanced Logistics IT-services, e.g.• Route planning & -optimization
• Tracking & Tracing, Fleet-Management
• Freight-Management, Order-Management
• Warehouse-Management, Supply-Chain-Management
•
Service Economics Faster, Cheaper, Better… Customization, push to pull processes
Demand for Improved Interoperability!
IT and AI in Logistics and Transport
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Incentive: Interest in „cheap“ & easy access to:
Enforcement: market pressure / competitiveness
/ customer expectation of:
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Key AI Predictions for 2020
• More money will pour into AI enterprise
projects than ever before
• A lot of AI projects will fail, in a costly manner.
• The way we interact with machines will
continue to shift towards voice.
• Robots will become more closely involved in
looking after our health and wellbeing.
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