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ARTIFICIAL INTELLIGENCE - 2012

Ústav přístrojové a řídicí techniky, Fakulta strojní, ČVUT v Praze Technická 4 , 166 07 Praha 6 , Tel: 00420 2 2435 2563 , Fax: 00420 2 3116414. ARTIFICIAL INTELLIGENCE - 2012. Jiří BÍLA. Main items of the lecture. 1. Artificial Intelligence - State of Art

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ARTIFICIAL INTELLIGENCE - 2012

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  1. Ústav přístrojové a řídicí techniky, Fakulta strojní, ČVUT v Praze Technická 4 , 166 07 Praha 6 , Tel: 00420 2 2435 2563 , Fax: 00420 2 3116414 ARTIFICIAL INTELLIGENCE - 2012 Jiří BÍLA

  2. Main items of the lecture • 1. Artificial Intelligence - State of Art • 2. Control of Complex Systems. • 3. Pattern Recognition - Computer Vision. • 4. Computer Aided ... (CAD, CAPP, CAM, CAQC, ..) • 5. HMI - Human Machine Interface • 6. Problem Solving. • 7. Autonomous systems (planetary modules).

  3. I. AI - BEGINNING AND EVOLUTION • Motivation and Objectives • Consequences of Cybernetics, Control Theory and Automation • „Stabilization of quantities (e.g., stabilization of temperature in this building on 23C)“ • „Stabilization of O2 concentration (in atmosphere)“ ?? … • Understanding to speech, text and patterns • „Communication in a natural language.“ • „How a robot goes out from a closed kitchen ?“ • „How to design a „for ever winning“chess automaton ?“ • Modeling of coordination structures (e.g., function of living ecosystems, function of the brain, modeling of the Mind ). • Unsolvable problems. E.g., „The method of stabilization of salt concentration in oceans“.

  4. I. AI - STATE OF ART • Control of Complex Systems (neuron models, fuzzy controllers). • Pattern Recognition, special sensors, …, computer vision, intelligent cameras. • Computer Aided (CAD, …, CASE). • Communication „Human- Machine“ (a natural language, …, artificial languages). • Problem Solving by expert systems (Instruction, consultation systems, help to human operators, monitoring, …). • Diagnostics (Fault Detection, …, Detection of Emergent Situations, ...). • Autonomous systems (…, robots detecting unexploded guns, …)

  5. II. Control of Complex Systems with hardly available models • Identification of Complex Systems by Artificial Neural Networks • Life Cycle of ANN : Learning (training),Testing, Operation. • Training Sequence (sequence of training pairs) • Fuzzy modeling. • Computing with uncertain variables and their values • Linguistic variables and linguistic values (e.g., temperature in the room, low, higher, unpleasant, very high, …) • Fuzzy Controllers. • Example of rule: IF(The control error is (Positive and Low) AND (The first derivation of Control error is (Positive and High))  THEN (Action is (Negative and Middle))

  6. Identification ofmathematical model of a parallel manipulator TRIPOD by a neural network Deployment of non traditional non linear dynamic neural units for identification of dynamics of parallel manipulator TRIPOD Parallel manipulator TRIPOD(VVZ J04/98 212200008, …, ČVUT )

  7. Motivation for the development of Non Conventional Neural Architectures The unavailability of information about the analyzed system from a trained network (e.g., a differential equation,…) A conventional Neural Network~ Black Box High Complexity and a great number of neural parameters of conventional neural networks (MLP,RBF,…)

  8. Non linear dynamic neural units for the parallel manipulator TRIPOD 3x • Each leg is identified by an autonomous non linear dynamic neural unit HONNU.

  9. Aproximovaná dynamika délky pístů Shodný průběh akčních veličin u1 u2 u3 Chyba dynamické aproximace Průběh délky pístů y1 y2 y3 Results of adaptive identification of non linear dynamic of TRIPOD Results of identification for the same actions and non equal load of manipulator platform

  10. An example of dynamic system with Fuzzy controller

  11. An example of dynamic system with Fuzzy controller

  12. III. Pattern Recognition, special sensors, …, computer vision, intelligent cameras. Development of Special Sensors. Representation of external world by means of artificial optical and tactile signals. : Sensors for surface pressure (diagnostics of walking), tactile sensors, sensors of force distribution in material structures (e.g., in over loaded parts of bones).

  13. Sensor for the measurement of pressure distribution on the surface The cellular sensor is connectible by a parallel port to computer and allows to activate 7500 cells 300 times per second.

  14. IV. Computer Aided Design (CAD, CAPP,…, CASE). • Classification of design phases : Early Design, Conceptual Design, Detailed Design • Classification of Design activities according to design objectives: Construction and technology (CAD), Design of production phases „in small“ (CA Production Planning), (CAM), Design of production phases „in large“ (CA Manufacturing), Design of assembly phases (CA Assembly), Design of systems for Quality Control (CA Quality Control), … , Design of Software products (CASE - Computer Aided Software Engineering). • Classification of Design according to computer support: Formal approach (Formal logic, expert systems), Deployment of special methodologies and CASE systems, Evolutionary approaches (e.g., gradual adaptation of prototypes. Genetic Algorithms).

  15. IV.Design of Information and Control Systems (ICS). • Design of ICS - without use of special methodologies and SW support - only in simple cases. • „ All designs end by a program“. Description of functions and activities of the program needs a special formal means. • Integration of activities by methodologies and SW support: Concetration of needed knowledge, analysis of information nvironment and controlled system, design of a sceleton of ICS, generation of ICS program code.

  16. Design of ICS by OMT (Object Modeling Technique, (Rubmbaugh, 1991) and UML (Unified Modeling Language (OMG, 1998)). • The OMT objective: To combine and to connect all important design phases from the description in natural language, trough analysis and of designed ICS till the design of ICS and generation of program code. • UML is a Multi-dimensional graphic-symbolical language that continues OMT methodology. • UML has 8 modeling strata: Use case model (1), Class Model (2), State Diagram(3), Interaction Diagram (4), Co-operation Diagram (5), Model of Activities (6), Component Model (7), Deployment Model (8). • Rough design scheme: Basic description of the problem (Expert)  Structured formulation of the problem (Knowledge Engineer)  OMT methodology  UML model  Implementation (CASE system and code generation)  Maintenance of the program product.

  17. Example of „translation“ of a sentence in natural language into class diagram by OMT: The sentence: „Center sf6 contains Jet Fans V515, …, V518 with reversation and 2 values control a Jet Fans V519 a V520 with reversation and continuously set up power.

  18. Design of ICS in Road Tunnel „Mrázovka“ in Prague. Application of OMT, UML and Rational Rose CASE. 9 controlled processes: Large ventilation, Small ventilation, Transport, Security, Energetics, Maintanence, Water sources, ...

  19. Scheme of Road Tunnel Mrázovka in Prague

  20. Class and State diagram for center SF6

  21. Pseudo-code of Delphi type written by special generator

  22. VI. Problem Solving by Expert Systems. Expert System contains Knowledge. Expert System is destined for interaction with human subject. Expert System contains Knowledge about ill Identifiable processes and objects - unavailable models. Basic operation for expert system is the Inference (not the computation).

  23. Support of Problem Solving • System of instructions. • The system manages a process by commands. • Qualitative models of actions„What/IF“. • Decision Support. • Intuitive synthesis. • An ideal form of the support. • Compromising way: Formal logic.

  24. Support of Problem Solving by Formal Logic • The description of all available knowledge that are relevant for the problem, the description of the environmentof the problem and of the goal of the problem solution by the language of formal logic of the first order (FOL) or in the language of propositional logic. • Example of the formula: xy (P(x,y)  Q(z)), • (P, Q … predicates, ,  … quantifiers, x, y, z … variables,  … operator of logic implication). • The solution algorithm works with the only one partial task: „Verify, please, if the proposed goal formula „A“ is consistent (there are no contradictions) in the set of the problem description „“ !“ ( A)  There are special algorithms for verification of consistency  A, (e.g., Theorem proving resolution Principle of Robinson (1953)). •  There were developed special programming languages for SW support of problem solving by Theorem – languages of the type PROLOG, LISP, POP, … .

  25. Support of Problem Solving by Expert Systems • The description of all available knowledge that are relevant for the problem, the description of the environment of the problem and of the goal of the problem solution is done i some representation language. Very often is used so called rule-based representation: • Rule: IF((C1, .., Cn, w1z, … , wnz, f))  THEN(D, g(w1a, …, wna)), • C1, .., Cn, conditions, sentences, propositions, • w1a, … , wna ... actualized weights, f … interaction function, D … result of inference, g(w1, …, wn) … the function for computing of the weight of the result • The rules are structuralized in chains, trees, (cycles), i.e. they for a knowledge base. • Basic modules of expert system:Knowledge base, Inference Engine, User Interface,Programme interface, Modul for Knowledge Acquisition, Explnation Modul • The problem is formulated (for ES) as a collection of conditions. After the Start of problem solving processInference Engineinvestigates the knowledge base till the state of satisfaction of the conditions.

  26. The support of diagnostics by expert systems Knowledge base consists of rules of the type:  IF((sp1= qi1 ) AND … AND (spn= qin ))  THEN( Porucha pn), sp1= qi1 means that symptom sp1 has value qi1 . Classification of symptoms: - overloading of technological limits (x  TM), - analysis of signal morphology, Example: Detection of faults in energetic system Herbertov

  27. Fault Diagnosis in Herbertov Area  The Exceedof technological limits: (P10 PMAX) AND (T12  TMAX)  fault of the valve near the pumps L33 or L35.

  28. Autonomous systems • Artificial intelligence without representation (i.e., without internal model), (Brooksians). • Instead of an internal model - reinforcement of reactivity. • Instead of long learning of human knowledge and habits - the development of adaptive reactive systems with instincts, reflexes and simple complexes of behavior. • The „Intelligence“ is developed in reaction with environment. • Problems of control is transferred into problems of emergent behavior (one of characteristics of Artificial Life). • Difficulties with learning and representation of knowledge are transformed to difficulties with communication (understanding and the interpretation of activities of the autonomous system). • Rozdíl signálů (bez výpadku a s výpadkem): detekce i lokalizace poruchy

  29. Autonomous systems • Example of autonomous mobot AM (mobile robot) - The Centre of Gerstner (CG), Faculty of Electrical Engineering, CTU in Prague.

  30. Means and strategies of AM CG

  31. Example of searching for the trajectory between obstacles - AM CG

  32. Conclusions Artificial Intelligence (AI) is not the same as the natural intelligence. There are the following successful fields of AI nowadays: In theory:  Approximation disciplines as fuzzy control, neural networks and genetic algorithms.  Contributions to psychology and cognitive science. In practice:  Diagnostics,  Contributions to Computer Aided Engineering (CAE) and other CA… .  Consultation systems,  Autonomous systems (planetar moduls, …).

  33. LITERATURA 1. P.H. Winston: Artificial Intelligence, MIT, Addison-Wesley Publishing Company, London, …, many additions from the first in 1977. 2. Banerji, R.: Artificial Intelligence, … 3. Nilson, S.: Artificial Intelligence, ... 2. J.R. Brooks: Intelligence without representation. AI, No. 47, 1991. s. 139-159 5. C. Langton: Artificial Life. Addison-Wesley Pub. Comp Inc., 1989. 6. A. Sloman: Can we design a Mind ? Keynote for AID 02 Conference, 2002. 7. K. Ueda: Emergent Synthesis. Artificial Intelligence in Engineering, No. 15, 2001. s. 319 - 327. Address: BÍLA Jiří, Prof. Ing. DrSc.Jiri.Bila@fs.cvut.cz, U 12110 FS ČVUT v Praze, Technická 4, 166 07 Praha 6

  34. END

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