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Artificial Intelligence in Real-time Systems

LAP 8780 and ISP 9010

Tallinn University of Technology

Professor Leo Motus

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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 AIo Expert systemso Case based reasoningo Bayesian networkso Behaviour based AI

Computational Intelligenceo Neural networkso Fuzzy systemso 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 optimisationo Swarm intelligence, e.g. ants

o A collective behaviour in decentralised, self-organised 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 knowledgeo Reasoning (problem solving), including the pattern

(or condition) matching problemso 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, etco Coalition of agents, multi-agent systemso Proactive components, social intelligence (?)

J. Ferber (1999) Multi-agent systems, Addison-WesleyR. 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

Simono Studies in swarm intelligence, and animal behaviouro Studies and experiments in molecular biology

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Opposing characteristics of the co-resident brain computers

von Neumann serial processor (symbol processing) is believed to operate in the left hemisphere of a human brain

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 Co-resident Brain Computers” Datamation, 1982, vol. 28, no.5, 122-136

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Newell-Simon functional model of a human brain

Motory actions

Env

iron

men

t

CognitionPerception

Env

iron

men

t

Buffers

Sensors

InterpreterCognitive processor

Internal memory l/s

External memory

Buffers

Human muscles

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Basic difference between conventional AI and AI in RT systems (1)

Technical or natural

system System based on AI

1. 2.

3.

4.

Conventional AI is explicitly human centric !

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Basic difference between conventional AI and AI in RT systems (2)

Technical or natural

systemSystem based on AI

A1

A2

H1, H2

H3, H4

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

A system comprising humans, computers, etc

Task i

Task 1 Task 2

Task n

Task 3

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A closer view on a task in a real-time system

Each task can be carried out by applying different methods, e.g.:o Methods based on “natural intelligence “ – i.e.

manuallyo 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 problemso Decrease the burden of mathematical modellingo Enable to use approximate non-linear methods for

reducing the dimensionality of input spaceo 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 approacho First-generation expert systems (e.g. the frustrated

banker) are a typical inductive approacho 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 caseo 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-theoryo 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 obtainedInductive 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 deadlineo 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 time-

deterministic way, and to continue reasoning if additional time becomes available

See, for instance, I.R. Chen “On applying Imprecise Computation to Real-

time 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

approacheso leads not necessarily to an approximation of the

inference treeo 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 time-aware 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 solutionso Typical problems – optimisation, adaptation, self-

learning, consistency check

2. Dynamic knowledge presentation and integrationo 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 time-critical environment

Two basic goals are to be achieved – time-aware behaviour and persistent assessment the quality of service

Induction based methods create less problems time-wise, and more problems quality-wise

Deduction based methods create less problems quality-wise, 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 domainso CAD, genetic algorithms, knowledge

representation, expert systems, control theory, software engineering, qualitative reasoning

2. New methods based on combination of AI and non-AI theorieso 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 problemso Intrinsic similarity of the design, analysis, and

verification of hardware and software designo 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)