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Artificial Intelligence in Real-time Systems LAP 8780 and ISP 9010 Tallinn University of Technology Professor Leo Motus 3.05.2017 1 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/ 3.05.2017 ©L.Motus, 2004 2 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 3.05.2017 ©L.Motus, 2004 3 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 3.05.2017 ©L.Motus, 2004 4 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) 3.05.2017 ©L.Motus, 2004 5 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. 3.05.2017 ©L.Motus, 2004 6 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 3.05.2017 ©L.Motus, 2004 7 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 3.05.2017 ©L.Motus, 2004 8 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 3.05.2017 ©L.Motus, 2004 Associative parallel processor (pattern processing) is believed to operate in the right hemisphere of a human brain 9 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 3.05.2017 ©L.Motus, 2004 10 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 3.05.2017 ©L.Motus, 2004 11 Newell-Simon functional model of a human brain Cognition Perception Sensors Internal memory l/s Interpreter Buffers 3.05.2017 Motory actions Cognitive processor External memory ©L.Motus, 2004 Buffers Human muscles 12 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 ! 3.05.2017 ©L.Motus, 2004 13 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 ! 3.05.2017 ©L.Motus, 2004 14 A view on a real-time system Environment Task 1 Task i Task 2 Task 3 Task n A system comprising humans, computers, etc 3.05.2017 ©L.Motus, 2004 15 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) 3.05.2017 ©L.Motus, 2004 16 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. 3.05.2017 ©L.Motus, 2004 17 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 3.05.2017 ©L.Motus, 2004 18 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 3.05.2017 ©L.Motus, 2004 19 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 3.05.2017 ©L.Motus, 2004 20 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 3.05.2017 ©L.Motus, 2004 21 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 3.05.2017 ©L.Motus, 2004 22 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 3.05.2017 ©L.Motus, 2004 23 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 3.05.2017 ©L.Motus, 2004 24 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) 3.05.2017 ©L.Motus, 2004 25 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 3.05.2017 ©L.Motus, 2004 26 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? 3.05.2017 ©L.Motus, 2004 27 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. 3.05.2017 ©L.Motus, 2004 28 Two different clusters of data for computing a posteriori distribution 3.05.2017 ©L.Motus, 2004 29 Approximate a posteriori probability density computed by Bayesian NN 3.05.2017 ©L.Motus, 2004 30 Scattering is used instead of probability density (possibilistic neural net) 3.05.2017 ©L.Motus, 2004 31 Possibility distribution as computed by a possibilistic neural net 3.05.2017 ©L.Motus, 2004 32 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 3.05.2017 ©L.Motus, 2004 33 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 3.05.2017 ©L.Motus, 2004 34 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 3.05.2017 ©L.Motus, 2004 35 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 3.05.2017 ©L.Motus, 2004 36 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 3.05.2017 ©L.Motus, 2004 37 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 3.05.2017 ©L.Motus, 2004 38 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) 3.05.2017 ©L.Motus, 2004 39 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 3.05.2017 ©L.Motus, 2004 40 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) 3.05.2017 ©L.Motus, 2004 41