Download In AI application in a real

Artificial Intelligence
in Real-time Systems
LAP 8780 and ISP 9010
Tallinn University of Technology
Professor Leo Motus
3.05.2017
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J. McCarthy “What is Artificial
Intelligence” (November 2004)
 science and engineering of making intelligent
machines, especially computer programs; need not
confine itself to methods that are biologically
observable.
 Intelligence is the computational part of the ability to
achieve goals in the world
 AI research started after WWII. Alan Turing’s lecture in
1947 – he was the first to decide that AI was best
researched by programming computers rather than
building machines
 http://www.formal.stanford.edu/jmc/whatisai/
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Schools of thought in AI (1)
 Conventional AI
o Expert systems
o Case based reasoning
o Bayesian networks
o Behaviour based AI
 Computational Intelligence
o Neural networks
o Fuzzy systems
o Evolutionary computation
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Schools of thought in AI (2)
 Conventional AI – behaviour based AI
A methodology for developing AI based on modular
decomposition of intelligence (e.g. Rodney Brooks):
o Robotics and intelligent agents (real-time
dynamic systems able to run in complex
environments
 Computationally leads to interaction-based model of
computation, e.g. super-Turing computation
See the course ISP 0012 – software dynamics
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Schools of thought in AI (3)
 Computational intelligence – evolutionary computation
Applies biologically inspired concepts, e.g. population,
mutation, survival of the fittest. These methods divide
into two:
o Evolutionary algorithms, e.g. genetic algorithms
o for search and optimisation
o Swarm intelligence, e.g. ants
o A collective behaviour in decentralised, selforganised systems (e.g. multi-agent systems)
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Examples of artificial intelligence
based techniques (1)
Basic (algorithm-centred) techniques stem from studying:
o Representation of shallow and deep knowledge
o Reasoning (problem solving), including the pattern
(or condition) matching problems
o Learning and adaptation (supervised and/or
unsupervised)
o Search (including data mining)
By combining the basic techniques more complex
problems can be solved – e.g. computer vision
The above-listed techniques are based on imitating
processes applied by biological creatures.
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Examples of artificial intelligence
based techniques (2)
Expansion of the domain where AI techniques were
applied, and deeper understanding of the essence of
“intelligence” has lead to non-algorithmic techniques:
o Agents, info-bots, nanobots, etc
o Coalition of agents, multi-agent systems
o Proactive components, social intelligence (?)
J. Ferber (1999) Multi-agent systems, Addison-Wesley
R. Brooks (1986) “A robust layered control system for a
mobile robot”, IEEE J.of Robotics and Automation
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Biological paradigms for Artificial
Intelligence and Real-time Control
Stem from the functioning principles of humans and
other biological species:
o Hypothetical division of functions between left and
right hemisphere
o A functional model of human brain by Newell and
Simon
o Studies in swarm intelligence, and animal behaviour
o Studies and experiments in molecular biology
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Opposing characteristics of the coresident brain computers
von Neumann serial
processor (symbol
processing) is
believed to operate in
the left hemisphere
of a human brain
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Associative parallel
processor (pattern
processing) is believed
to operate in the right
hemisphere of a
human brain
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Comparison of functions of the
hemispheres (human brain)
The computation and/or reasoning is:
in the left hemisphere
in the right hemisphere
- linear
- non linear
- time sequential
- time independent
- batch oriented
- multi-tasking
- stacked interrupts
- random parallel execution
- word/symbol oriented
- pattern oriented
- non-intuitive
- highly intuitive
- structured memory
- associative memory
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Comparison of functions of the
hemispheres (human brain)
The computation and/or reasoning is:
in the left hemisphere
in the right hemisphere
- cumulative correlation
- instantaneous multiple
correlation
- incremental learning
- non-sequential learning
- sensory dependent
- sensory independent
V. Rauzino, “ Some opposing characteristics of the Coresident Brain Computers” Datamation, 1982, vol. 28,
no.5, 122-136
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Newell-Simon functional model of a
human brain
Cognition
Perception
Sensors
Internal memory l/s
Interpreter
Buffers
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Motory
actions
Cognitive
processor
External memory
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Buffers
Human
muscles
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Basic difference between conventional
AI and AI in RT systems (1)
1.
Technical
or natural
system
2.
4.
3.
System based
on AI
Conventional AI is explicitly human centric !
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Basic difference between conventional
AI and AI in RT systems (2)
A1
H1, H2
Technical
or natural
system
H3, H4
System based
on AI
A2
Humans have just a role of a supervisor
in AI applications in Real-time systems !
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A view on a real-time system
Environment
Task 1
Task i
Task 2
Task 3
Task n
A system comprising humans, computers, etc
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A closer view on a task in a realtime system
Each task can be carried out by applying different
methods, e.g.:
o Methods based on “natural intelligence “ – i.e.
manually
o Methods based on Science (e.g. mathematics,
control theory, etc)
o Methods based on “artificial intelligence” – i.e. crisp
theory based reasoning, approximate methods of
reasoning (e.g. neural nets, fuzzy logic), distributed
intelligence methods (e.g. agents)
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Intelligent methods (+)
Natural and artificial intelligence based methods are good
since they:
o Provide efficient solution to a many computationally
complex problems
o Decrease the burden of mathematical modelling
o Enable to use approximate non-linear methods for
reducing the dimensionality of input space
o Are capable of drawing unexpected conclusions
and applying unconventional methods on spot.
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Intelligent methods (-)
Natural and artificial intelligence based methods are not
always applicable because:
o Only probabilistic estimates are available for the
quality of obtained solutions (they are approximations
of the “scientific” solutions)
o Time for obtaining a solution is indeterminate (the
case of deduction based methods)
o Due to insufficient educational background those
methods are too often handled as “black boxes” –
hence no guaranteed result
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Intelligent methods – the case of
conventional AI applications
 Many independent tasks are solved simultaneously,
or rather a single task at a time
 The environment cannot influence task execution
process – truth values are independent of time and
events, occurring in the environment or in the other
tasks
 Frequent use of backtracking – task execution time is
indeterminate
 Goals and sub-goals of tasks are static, and are to be
fixed before the execution of the task starts
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Intelligent methods – the case of AI
methods in real-time systems
 Many, inter-dependent tasks are to be solved
simultaneously (forced concurrency)
 The environment can influence the task execution
process – truth values may change dynamically,
depending on time and events occurring in the
environment
 Time for execution of a tasks is often strictly limited
 Goals and sub-goals for tasks may be determined
dynamically (during the task execution) – only a strategic
goal is usually fixed before the execution starts
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Names used for AI methods are not
self-explaining and straightforward
Most of the methods and tools used have historical names
and are in-between of pure deductive and pure inductive
methods.
For instance, expert systems:
o The first-order predicate calculus is a typical expert
system and represents a classical deductive approach
o First-generation expert systems (e.g. the frustrated
banker) are a typical inductive approach
o Second generation expert system (a mixture of deep
and shallow knowledge) are in between the two
approaches
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Quality of a task’s solution
In conventional AI application quality means logical and
quantitative correctness of a solution – normally a vector
comprising, e.g. precision, risk estimate, cost, etc.
In AI application in a real-time system timeliness is
added as the highest priority component of the quality
vector
Conventional quality-wise – more promising are
deductive methods
Time-wise – more promising are inductive methods
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Intelligent methods – deductive
approach
Paradigm -- top-down approach;
from a general case to a specific case
o humans build a non-contradicting theory, based on
deep knowledge and experimental data
o Specific problems are stated (usually by humans) as
special cases of this theory, and then solved by
computers
Examples: theorem provers, structural synthesis of
programs
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Intelligent methods – inductive
approach
Paradigm – bottom-up approach;
from a specific case to a general case
o Humans provide meta-theory
o Based on meta-theory and a set of examples
(problems with solutions), computers (or humans)
build specific theories that resolve a class of
problems
Examples: neural nets, inductive synthesis of programs
Note: induction and co-induction
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Comparing deductive and inductive
approaches
Advantages:
Deductive methods provide guaranteed quality of the
solution, if obtained
Inductive methods have short and rather
deterministic execution time
Disadvantages:
Deductive methods have indeterminate solution time
and high resource requirements (labour-consuming)
Inductive methods have usually unknown quality of
the solution, formation of the learning set is not easy,
learning time is lengthy (building a special theory)
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Approximate reasoning (1)
Pragmatic goals:
o to obtain interim result in the reasoning process
before any given deadline
o be able to continue reasoning if time and other
resources permit
Implicit assumption – the quality of the reasoning
outcome (and interim results) improves proportionally
with the given time and resources
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Approximate reasoning (2)
Also known as:
imprecise computing, any-time algorithms, progressive
reasoning, etc.
Basic idea – to make reasoning results available in timedeterministic way, and to continue reasoning if additional
time becomes available
See, for instance,
I.R. Chen “On applying Imprecise Computation to Realtime AI Systems”, The Computer Journal, vol.38, no.6,
1995, ,434 – 442 (kataloog lugemisvara)
Reflex-based approach – a way out for real-time systems?
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Approximate reasoning (3)
A simple example of approximate reasoning – forecasting
the trends based on observations:
o Based on recursive computation of a posteriori
probability densities
o Based on recursive adjustment of membership
functions (possibilities), related to many-valued logic
and case-base reasoning
Approximate solution methods (Bayesian neural nets and
possibilistic neural nets) are used to reduce
computational complexities.
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Two different clusters of data for
computing a posteriori distribution
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Approximate a posteriori probability
density computed by Bayesian NN
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Scattering is used instead of probability
density (possibilistic neural net)
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Possibility distribution as computed
by a possibilistic neural net
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Reflex-based approach to
approximate reasoning (1)
Imitates the behaviour of biological creatures acting in hard
real-time – e.g. car driving, collective games (karate,
dancing), riding a bicycle
Observation – an experienced driver makes complex and
high quality decisions in a short time, a novice driver
cannot reach such decisions (even if given unlimited
time)
Hypothesis -- decisions and actions of humans in hard
real-time are based on reflexes rather than on
conventional reasoning
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Reflex-based approach to
approximate reasoning (2)
Reflex-based approach to reasoning:
o should combine deductive and inductive
approaches
o leads not necessarily to an approximation of the
inference tree
o creates shortcuts on the inference tree by modifying
inference rules, a set of axioms, or both
A weak similarity – with time deterministic case-base
reasoning method as used in the BRIDGE project
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AI applications in Real-time
systems (examples)
 Navigation tasks, computer vision related tasks,
performance of AUV, etc
 On-line assessment of strategies, generation of
alternative strategies and/or goals
 Coordinated work of multiple agents, especially timeaware agents, agent coalitions and their competition
 Sensor fusion, feature fusion, remote monitoring,
safety, reliability and fault-tolerant problems.
The Farm project provides plenty of possibilities to study
and test additional examples
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Generic groups of AI applications
in Real-time systems (1)
1. Automatic generation and/or assessment of
alternative solutions
o Typical problems – optimisation, adaptation, selflearning, consistency check
2. Dynamic knowledge presentation and integration
o Typical problems – sensor fusion, process
monitoring and diagnosis, reliability and safety
backing, pattern forming, pattern matching
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Generic groups of AI applications
in Real-time systems (2)
3. Coordinated work of multiple agents (proactive
components)
o Typical problems – interaction of agents, multiple
goal system, dynamic change of goals, network for
interacting agents
Generic groups ordered by increasing complexity :
group 1  group 2  group 3
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Characteristic issues when
applying AI in Real-time systems
 AI based methods cannot be applied independently and
must cooperate with parts of a time-aware, or timecritical environment
 Two basic goals are to be achieved – time-aware
behaviour and persistent assessment the quality of
service
 Induction based methods create less problems timewise, and more problems quality-wise
 Deduction based methods create less problems qualitywise, and more problems time-wise
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Ways of interdisciplinary integration
of AI and non-AI methods (1)
1. Mechanical combination of methods from various
domains
o CAD, genetic algorithms, knowledge
representation, expert systems, control theory,
software engineering, qualitative reasoning
2. New methods based on combination of AI and
non-AI theories
o
approximate solution of hitherto not applicable
mathematical problems (Pontryagin’s maximum
principle  two-point boundary value problem 
neural nets)
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Ways of interdisciplinary integration
of AI and non-AI methods (2)
3. Use of the abstract nature of AI methods to clarify
the essence of problems
o Intrinsic similarity of the design, analysis, and
verification of hardware and software design
o Necessity to apply different methods for solving
different problems – strengths and weaknesses of
algorithm-centred and interaction-centred
computing
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Additional reading on Artificial
Intelligence in Real-time system
 Journals available in Department of Computer Control (TTU)
o Engineering Applications of Artificial Intelligence
(Elsevier)
o Intelligent Computer-Aided Engineering (IOC)
o Real-time Systems – Journal of Time-critical Computing
(Kluwer)
 Other Journals
o Journal on Autonomous Agents and Multi-agent systems
(Kluwer)
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