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Artificial Intelligence System Designer 4GN ISP RAS Alexander Zhdanov Artificial Intelligence Pattern recognition Data mining Image recognition Automated reasoning Expert systems Prediction Automated control Problems solved by means of AI systems: Approaches used:

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artificial intelligence
Artificial Intelligence
  • Pattern recognition
    • Data mining
    • Image recognition
  • Automated reasoning
  • Expert systems
  • Prediction
  • Automated control

Problems solved by means of AI systems:

Approaches used:

  • Artificial Neural networks
  • Fuzzy logic
  • Reinforcement learning
  • Stochastic approaches
  • Structural representation
aac framework for ai system design
AAC Framework for AI system design
  • Explicitly deals with
    • Pattern recognition
    • Knowledge base formation
    • Prediction
    • Automated analysis
  • May be based on
    • Determined chaos systems
    • 3rd generation neural networks
    • Genetic algorithms
    • Stochastic methods
  • Open for other sophisticated techniques
  • Well-suited for:
    • Pattern recognition systems
    • Expert systems
    • Data mining system
    • Adaptive control systems
aac framework for ai system design4
AAC Framework for AI system design

AAC Comparison with other AI-approaches:

  • Artificial neural networks - perform only patter recognition or approximation and demand a priori learning. AAC systems have abilities for self-control
  • Fuzzy logic systems – demand a priori formulated fuzzy rules. AAC systems deduce rules themselves and corrects them if necessary
aac framework for ai system design5
AAC Framework for AI system design

For example, the AdCAS system for car suspension adaptive control could not be created simply on basis of another method: artificial NN, reinforcement learning, fuzzy logic or any another approach.

aac framework applicability
AAC Framework applicability
  • Pattern recognition systems
  • Prediction, forecasting systems
  • Expert systems, decision making
  • Adaptive control systems
  • Highly adjustable to problem domain or context
universality of a system
Universality of a system

“Applicability of a method is inversely proportional to its universality”

It is impossible to create universal control system for ANY customers and ANY problem, because its parameters have to depend on given objects

  • Parameters of CS, which are independent from CO
  • 1) The structure of the CS operation;
  • 2) The ways in which the CS subsystems are constructed – the recognition system, knowledge base and other subsystems;
  • 3) The models of the neuron-like elements of which the the CS subsystems are constructed; etc.
  • Parameters of CS, which dependent from CO
  • 1) The input and output variables and their characteristics;
  • 2) The rules of pattern formation which will be required for the control;
  • 3) The rules of knowledge formation in the knowledge base
  • 4) The qualitative criteria for the evaluation of the possible states of the CS, for the control quality evaluation, for the determination of the goal functions; etc.
computer aided system engineering
Computer Aided System Engineering
  • Pros
    • Produces highly customizable solutions
    • Ease of use: does not require hardcore programming skills
    • Adaptability
    • Flexibility
    • Development of end-user solutions
  • Cons (requires from developers)
    • High level of abstraction on analysis stage
    • Deep understanding of system principia
    • Ability to translate abstraction into concrete notions
case for design of ai systems

Computer model of given application

Model of sensors

AAC

method

Programming

Prototype of CS

Model of CO

Model of actuators

The software Tools

CASE for design of AI systems
proposal 1 development of case for design of ai systems based on aac method
Proposal #1: Development of CASE for design of AI systems based on AAC method

Main features:

  • Drastically reduces time and resources required for development
  • Explicit AI orientation
  • Export/import interfaces with simulation software and with hardware
  • Advanced visualization and analysis techniques
  • Easy to use for non-experienced programmer
  • Makes process of AI system design more transferable
  • Open interfaces
three main phases of applied ai systems projecting for various tasks

Control System

Controlled Object

Phase I.

CS

Sensors

Sensors

CO

CO

Actuators

Actuators

Environment

Environment

  • The projection and development of the CS prototype and its testing on program models of the CO, Sensors and Actuators. (1-2 years)

Phase II.

CS

Sensors

CO

Actuators

Environment

  • The debugging of the CS prototype on real CO, sensors, actuators or their physical models.

Phase III.

CS

  • Building-in of the CS into the real CO, where the CS is implemented on real (on-board) processor.
Three main phases of applied AI systems projecting for various tasks
proposal 2 aac based multiagent system architecture
Proposal #2: AAC-based multiagent system architecture

Development of multiagent system architecture based on AAC

Main features of such system:

  • Explicitly distributed
  • AI-based (in sense of AAC)
  • Self-monitoring, Self-adaptability, Self-manageability, Self-learning
  • Secure
  • Adaptable to heterogeneous environment
  • Decentralized control
  • Flexibility
  • Eco-system principles based
thank you for your time
Thank you for your time

Welcome to discussion