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David Gamez IUA Week 7 Autumn 2007 1
Artificial Intelligence (AI)
David Gamez IUA Week 7 Autumn 2007 2
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.
David Gamez IUA Week 7 Autumn 2007 3
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!
David Gamez IUA Week 7 Autumn 2007 4
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.
David Gamez IUA Week 7 Autumn 2007 5
GOFAI
David Gamez IUA Week 7 Autumn 2007 6
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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David Gamez IUA Week 7 Autumn 2007 7
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.
David Gamez IUA Week 7 Autumn 2007 8
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) ).
David Gamez IUA Week 7 Autumn 2007 9
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.
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Agent-based Systems
David Gamez IUA Week 7 Autumn 2007 11
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.
David Gamez IUA Week 7 Autumn 2007 12
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.
David Gamez IUA Week 7 Autumn 2007 14
SAFEGUARD
David Gamez IUA Week 7 Autumn 2007 15
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.
David Gamez IUA Week 7 Autumn 2007 16
SAFEGUARD
David Gamez IUA Week 7 Autumn 2007 17
BiologicallyInspired AI
David Gamez IUA Week 7 Autumn 2007 18
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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David Gamez IUA Week 7 Autumn 2007 19
Neural Networks
http://faculty.washington.edu/chudler/color/pic1an.gif
http://research.yale.edu/ysm/images/78.2/articles-neural-neuron.jpg
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Artificial Neural Network
David Gamez IUA Week 7 Autumn 2007 21
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.
David Gamez IUA Week 7 Autumn 2007 22
Training with Back Propagation
David Gamez IUA Week 7 Autumn 2007 23
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.
David Gamez IUA Week 7 Autumn 2007 24
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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David Gamez IUA Week 7 Autumn 2007 25
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.
David Gamez IUA Week 7 Autumn 2007 26
Artificial Ants
David Gamez IUA Week 7 Autumn 2007 27
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.
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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.
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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.
David Gamez IUA Week 7 Autumn 2007 30
Genetic Algorithms
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David Gamez IUA Week 7 Autumn 2007 31
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.
David Gamez IUA Week 7 Autumn 2007 32
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.
David Gamez IUA Week 7 Autumn 2007 33
Genetic Algorithm Example
David Gamez IUA Week 7 Autumn 2007 34
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.
David Gamez IUA Week 7 Autumn 2007 35
MachineLearning
David Gamez IUA Week 7 Autumn 2007 36
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!
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Case Study: Eurobios
David Gamez IUA Week 7 Autumn 2007 39
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.
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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.
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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.
David Gamez IUA Week 7 Autumn 2007 42
Agent-based Risk Simulation
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David Gamez IUA Week 7 Autumn 2007 43
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
David Gamez IUA Week 7 Autumn 2007 44
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.
David Gamez IUA Week 7 Autumn 2007 45
Waste Optimizer
David Gamez IUA Week 7 Autumn 2007 46
Stock Market Prediction
David Gamez IUA Week 7 Autumn 2007 47
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.
David Gamez IUA Week 7 Autumn 2007 48
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.
David Gamez IUA Week 7 Autumn 2007 50
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.
David Gamez IUA Week 7 Autumn 2007 51
Entertainment Industry
David Gamez IUA Week 7 Autumn 2007 52
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.
David Gamez IUA Week 7 Autumn 2007 53
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.
David Gamez IUA Week 7 Autumn 2007 54
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.
David Gamez IUA Week 7 Autumn 2007 56
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.
David Gamez IUA Week 7 Autumn 2007 57
Massive Software
David Gamez IUA Week 7 Autumn 2007 58
Massive Crowd Simulations
David Gamez IUA Week 7 Autumn 2007 59
Deep Blue
David Gamez IUA Week 7 Autumn 2007 60
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
David Gamez IUA Week 7 Autumn 2007 62
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.
David Gamez IUA Week 7 Autumn 2007 63
Machine Consciousness
David Gamez IUA Week 7 Autumn 2007 64
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.
David Gamez IUA Week 7 Autumn 2007 65
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).
David Gamez IUA Week 7 Autumn 2007 66
CRONOS and SIMNOS
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SpikeStream
David Gamez IUA Week 7 Autumn 2007 68
‘Conscious’ Neural Network
David Gamez IUA Week 7 Autumn 2007 69
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.
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COG
David Gamez IUA Week 7 Autumn 2007 71
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.
David Gamez IUA Week 7 Autumn 2007 72
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
David Gamez IUA Week 7 Autumn 2007 74
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.
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Darwin
David Gamez IUA Week 7 Autumn 2007 76
Conclusions
David Gamez IUA Week 7 Autumn 2007 77
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.
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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
• ?
David Gamez IUA Week 7 Autumn 2007 80
Resources
David Gamez IUA Week 7 Autumn 2007 81
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/2004/09/30/AIforGameDev.html
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Resources
• Video on artificial ants: http://www.youtube.com/watch?v=ehEzRUu4_RM
• 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.html
• Slide show on genetic algorithms: http://www.informatics.indiana.edu/fil/CAS/PPT/Davis/sld001.htm
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Resources
• Evolution of a neural network to control a toy car: http://togelius.blogspot.com/2006/04/evolutionary-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.