Artificial Intelligence (AI)

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David Gamez IUA Week 7 Autumn 2007 1

Artificial Intelligence (AI)

Overview

• A brief introduction to the field.

• Won’t go too heavily into the theory.

• Will focus on case studies of the application of AI to business.

• AI and robotics are closely linked – more on this next week.

What is AI?

• Attempt to build and understand intelligent entities.

• Want computers and robots that can solve complex problems for themselves.

• One of the dreams of AI has been to build systems that are more intelligent than humans.

• However, progress has been very slow!

What is AI?

• Name was coined in 1956 at a two month workshop in Dartmouth attended by major people in the field.

• Early work on symbolic reasoning, searching, formal representations.

• Hit major limitations.

• More recently machine learning and biologically inspired approaches have become popular.

GOFAI

• Stands for Good Old Fashioned Artificial Intelligence.

• Knowledge is represented symbolically and the system attempts to reason using the symbolic knowledge.

• For example, a variety of different formal logics have been used in GOFAI.

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Logic

• Formal way of representing the state of the world and reasoning about it.

• Logic programming systems, such as Prolog, compute the consequences of the axioms and rules in order to answer a query.

Logic Example

• A Stagirite teacher of a Macedonian conqueror of the world is a disciple and an opponent of a philosopher admired by Church Fathers.

• ( x)( y)( z)(isStagirite(x) /\ teaches(x,y) isMacedonian(y) /\ conquersTheWorld(y) isDiscipleOf(y,z) /\ isOpponentOf(y,z) /\

isAdmiredByChurchFathers(z) ).

Problems with Logic

• Problems were soon encountered about the lack of common sense in logic-based systems.

• Lenat’s Cyc attempted to store common sense, but now considered a failure.

• GOFAI approaches are now largely obsolete.

Agent-based Systems

Agents

• Software entities that perceive their environment and act on it.

• Can be partially or completely autonomous.

• Agent systems contain a number of different types of agent cooperating to solve problems by passing messages to one another.

• Can handle problems that are difficult with conventional computing techniques.

• Can also be used for modelling complex scenarios.

Simple Agent

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SAFEGUARD Agent System

• EU Safeguard project that aimed to protect the management networks of critical infrastructures, such as electricity and telecommunications networks.

• Defend against attacks, failures, accidents.

• Built an agent system with a variety of different agents.

SAFEGUARD

• Agents designed to:

– Monitor network traffic for anomalous activity.

– Monitor critical computer files.

– Kill dangerous processes.

– Correlate information from other agents.

– Change firewall policy.

– Wrap the intrusion detection system.

SAFEGUARD

Biologically Inspired AI

Artificial Neural Networks

• Loosely inspired by the brain.

• Number of neurons connected to each other.

• Each connection has a particular weight.

• Activity in one neuron is passed to the connected neurons.

• Variety of types and network architectures.

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Neural Networks

http://faculty.washington.edu/chudler/color/pic1an.gif

http://research.yale.edu/ysm/images/78.2/articles-neural-neuron.jpg

Artificial Neural Network

Artificial Neural Networks

• A network is trained by exposing it to the training data and adjusting the weights so that its output is correct.

• After enough examples, the network can be used to classify new data.

• Good way of learning complex nonlinear functions.

Training with Back Propagation

Spiking Neural Networks

• More biologically inspired and much more realistic neural model.

• Used to model how the brain works – for example the Blue Brain project.

• Also starting to be used for robotics and machine learning.

Artificial Ants

• Real ants wander randomly. When they find food they return to their colony while laying down pheromone trails.

• If other ants find such a path, they are likely to follow the trail, returning and reinforcing it if they find food.

• Over time the pheromone trail starts to evaporate, thus reducing its attractive strength.

• The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate.

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Artificial Ants

• A short path gets marched over faster, and thus the pheromone density remains high as it is laid on the path as fast as it can evaporate.

• Thus, when one ant finds a good (i.e. short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads to all the ants following a single path.

• The idea of the ant colony algorithm is to mimic this behaviour with "simulated ants" walking around a model of the problem to be solved.

Artificial Ants

Artificial Ants - Applications

• Finding the shortest path through a network.

• Gene analysis.

• Data clustering

– Extend the model by having ants carry pieces of data.

– Ant is programmed with a rule that makes it likely to drop data by similar pieces of data.

– Clusters are built up by the random movements of the ants.

Genetic Algorithms

• Living systems solve problems by evolving new creatures.

• Mating produces millions of variations.

• The variations that are most successful survive to become the parents of the next generation.

Genetic Algorithms

• Work in a similar way to natural evolution.

• The thing that you want to evolve is encoded as a gene.

• The gene is used to produce ‘organisms’.

• The fitness of the organisms is evaluated on the specified task.

• The best organisms are ‘mated’ by randomly combining their genes to produce new organisms.

• The process is repeated until the problem is solved.

Genetic Algorithms

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Genetic Algorithm Example

• Genetic algorithms can be used to evolve a neural network to control a toy car.

• The neurons and connections are encoded as a list of 1’s and 0’s.

• Initially a large number of random genomes are produced.

• Neural networks are constructed based on these random genomes.

Genetic Algorithm Example

• Each of these networks is used to drive the toy car.

• The best networks are selected for the next generation.

• These networks are ‘mated’ in pairs by randomly combining their ‘genes’, in a process called crossover.

• The genes may be mutated as well.

• New neural networks are constructed based on the new genomes and tested on the car.

Genetic Algorithm Example

Applications of Genetic Algorithms

• Evolving controllers.

• Optimizing algorithms, production processes, etc. where you have a large number of parameters.

• Can be used to design products, such as semiconductors and turbines.

• Used in genetic programming where a program is evolved for a particular function.

Machine Learning

• Application of AI techniques to identify patterns and classify data.

• Learning is applied in a very restricted way to solve one particular problem – no aspiration towards human-level intelligence.

• Uses a variety of AI techniques, such as neural networks, genetic algorithms, etc.

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Machine Learning Examples

• Identification of anomalous patterns of behaviour in security cameras.

• Data mining.

• Medical diagnosis.

• Face recognition

• Natural language processing.

• And many many more!

Case Study:

Eurobios

• Company based in London, France and Australia (www.eurobios.com)

• Uses AI techniques to model different aspects of a company’s workflow, risks, waste collection, etc.

• Sells software and solutions based on this work.

Agent-based Risk Simulation

• Risk Simulator is a strategic agent-based simulation solution :

– Models internal company processes – Simulates scenarios, taking into account the

mapping of risks to processes, and combined impacts

– Analyses critical paths of risk propagation though the enterprise.

– Can select and replay critical scenarios.

Agent-based Risk Simulation

• Identifies risks or combinations of risks that cannot be found by traditional statistical methods

• Quantifies risks, even without complete of data

• Identifies checkpoints in order to reduce risk impacts

• Details this model at operational level so as to master critical processes.

Agent-based Risk Simulation

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Waste Optimizer

• Generates new routes to collect waste from an area.

• Can specify:

– Types of vehicle available – Time, Distance and Cost

– Refuse, Recycling, Green waste services – Variation in frequency & volume (participation) – Urban, rural or mixed regions

Waste Optimizer

• Provides analysis on how to reduce the number of rounds by reducing time taken & distances travelled.

• Can also optimise existing rounds whilst maximising the productivity of each vehicle.

• Has several budgeting capabilities to analyse the cost impact of providing new services or making changes to existing services.

• Probably based partly on artificial ant AI.

Waste Optimizer

Stock Market Prediction

• In 2005 a third of all stock trades in the US were driven by automatic algorithms.

• AI is used to look for hidden market patterns.

• Prediction is key to making money on the stock market.

• For example there could be long term similarities in the price movements of Microsoft and IBM.

• When their prices diverge, investors sell the expensive stock and buy the cheap stock, betting that historical patterns will eventually push them back into synchronicity.

Yamaichi Fuzzy Fund

• The Yamaichi Fuzzy Fund uses the so called neurofuzzy approach to make financial forecasts.

• It handles 65 industries and a majority of the stocks listed on Nikkei Dow and contains approximately 800 fuzzy rules in its expert system.

• Rules are determined monthly by a group of experts and modified by senior business analysts as necessary.

• The neural network is used to teach the application using historical trading data.

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Yamaichi Fuzzy Fund

• The application uses both the fuzzy expert system and the neural network to create statements like: "The trading situation today is similar to this pattern, thus we need to do this, this and this".

• The system was tested for two years, and its performance in terms of return and growth exceeded the Nikkei Average by over 20%.

• While in testing, the system recommended to

"sell" 18 days before the Black Monday of 1987.

The system went into commercial operations in 1988.

First Quadrant

• First Quadrant, an investment firm in Pasadena, California, relies on genetic algorithms to help it manage $5 billion worth of investments.

• Since 1993, when it began to use the technique for commercial systems, the programs have earned the company $25 million.

Entertainment Industry

Games

• AI is used extensively in computer games.

• Board games, such as chess.

• Other characters, vehicles, spaceships etc. are controlled by AI.

• Neural networks can be used for:

– Control

– Threat assessment – Attack or Flee – Anticipation

• Many other AI techniques used as well.

Emergent Behaviour in Games

• AI has been used to give game characters emergent behaviour.

• For example, the ‘creature’ in Black and White can be taught:

– simple tasks like keeping the village store full of food and wood;

– a range of beneficial, benign, or violent acts: anything from what and when to eat to how to attack an enemy's villagers using trees as weapons.

– fighting skills for one on one battles with other creatures,

– its attack and defence abilities can each be trained and improved.

Black and White

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Black and White

• The Creature is taught using a slap/stroke system.

• If the Creature does something the player does not want it to do, the player can slap the creature.

• If the Creature does something the player approves of, he can stroke the Creature.

• The Creature remembers whether or not you rewarded it for an action, and will not do things you slapped it for.

• Stroking results in just the opposite, as the creature will frequently do things you stroked it for.

Special Effects

• Massive is a software package used in the visual effects industry.

• Can create thousands - or millions - of agents that all act as individuals.

• Through the use of fuzzy logic, every agent to responds individually to its surroundings.

• These reactions affect the agent's behaviour, changing how they act and controlling motion- captured animations to create a realistic looking character.

Massive Software

Massive Crowd Simulations

Deep Blue

• Chess-playing computer developed by IBM.

• 30 processors capable of calculating 11.38 gigaflops.

• Also contained 480 special purpose chess chips.

• Beat world champion Gary Kasparov in 1997.

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Deep Blue vs Kasparov

Deep Blue

• Supercomputer using brute force to calculate possible moves.

• Evaluated 200 million positions per second.

• Deep Blue also had records of 700,000 past master games.

• The programmers studied Kasparov’s previous games in great detail.

Machine Consciousness

• Emerging research area inspired by recent interest in consciousness.

• Trying to produce:

– Machines with external behaviour associated with consciousness.

– Machines with cognitive characteristics associated with consciousness.

– Machines with an architecture that is claimed to be a cause or correlate of human

consciousness.

– Phenomenally conscious machines.

CRONOS Project

• 3 year project at the University of Essex and University of Bristol.

• First large project to be funded on machine consciousness.

• Includes:

– CRONOS hardware robot – SIMNOS virtual robot

– SpikeStream neural simulator (the ‘brain’ of the system).

CRONOS and SIMNOS

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SpikeStream

‘Conscious’ Neural Network

COG

• Early system developed by Brooks et. al.

at MIT.

• Basically an example of GOFAI.

• Could manage many separate behaviours, but the whole system was badly

integrated.

• The philosopher Daniel Dennett gave a philosophical analysis of the

consciousness of COG.

• Now a museum piece.

COG

IDA

• Naval dispatching system that assigns sailors to new postings.

• Based on the global workspace model of consciousness.

• Processes compete to place their information on the global workspace.

• Information on the global workspace is broadcast to all other processes.

Cyberchild

• Project searching for the neural correlates of consciousness.

• Simulated nervous system controlling a virtual baby.

• Child learns to get milk by vocalising its state.

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Cyberchild

Darwin

• Simulated neural architecture closely based on the brain with 200,000 neurons.

• Controls a robot over a wireless link.

• Robot learns to recognise features of its environment and develops preferences based on its emotional state.

Darwin

Conclusions

• AI is good at solving well defined problems.

• Also good for modelling complex situations.

• Can outperform humans on well defined tasks, such as chess.

• Many commercial applications of AI.

Conclusions

• Scientists are working on better AI – including machine consciousness.

• However, human level intelligence is a long way off.

• So no need to worry about Terminator and iRobot scenarios!

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Questions

• ?

Resources

• Introduction to AI:

http://library.thinkquest.org/2705/

• Cyc logic-based system:

http://www.cyc.com/

• Textbook on neural networks:

http://www.statsoft.com/textbook/stneunet.

html

• Applications of neural networks in games:

http://www.onlamp.com/pub/a/onlamp/200 4/09/30/AIforGameDev.html

Resources

• Video on artificial ants:

http://www.youtube.com/watch?v=ehEzRUu4_R M

• Artificial Ants solve networking problems:

http://news.bbc.co.uk/1/hi/sci/tech/1537645.stm

• Overview of genetic algorithms and their applications:

http://www.talkorigins.org/faqs/genalg/genalg.ht ml

• Slide show on genetic algorithms:

http://www.informatics.indiana.edu/fil/CAS/PPT/

Davis/sld001.htm

Resources

• Evolution of a neural network to control a toy car:

http://togelius.blogspot.com/2006/04/evolutionar y-car-racing-videos.html

• Eurobios: http://www.eurobios.com/

• Artificial intelligence and the stock market:

http://www.iht.com/articles/2006/11/23/business/

trading.php

• Stock market and genetic algorithms:

http://www.newscientist.com/article/mg14419543 .800.html

• More about the CRONOS project:

http://www.cronosproject.net.