Intelligent systems

This lesson comprises eight (8) master classes focusing on:

• Decision support systems
• Expert systems
• Intelligent agents
• Search engines
• Inference engines
• Artificial Intelligence

Content:

Systems and their applications

• Investigate applications of decision support systems
• Describe categories of decision-making within decision support systems, including:
  • an infinite set of instructions where judgement may be required (unstructured)
  • use of a specified set of finite instructions (semi-structured)
  • automated decisions (structured)
• Investigate common applications of expert systems, including:
  • diagnosis
  • monitoring
  • process control
  • scheduling and planning
• Describe key features of an expert system, including:
  • knowledge base
  • inference engine, including forward chaining and backward chaining
  • user interface (UI)
• Research factors that have allowed expert systems to advance from rule-based systems to integrate probability, including:
  • processing power,
  • data availability and access
  • hardware costs
  • neural networks
  • webometrics
• Describe how people’s changing needs have shaped the proliferation of expert systems, including:
  • the Internet of Things (IoT)
  • Internet of Me (IoMe)
  • Industry 4.0
• Describe the operation of simplistic and complex intelligent agents used by search engines, including predictive search strings and the use of voice assistants to initiate interaction
• Compare techniques used by different inference engines, including:
  • truth maintenance
  • hypothetical reasoning
  • heuristic knowledge and fuzzy logic
  • ontology classification
• Investigate the hardware used in an intelligent system, including:
  • biometrics
  • haptics
  • touch and gesture
  • virtual and augmented reality (VR/AR)
  • voice and sound
  • microcontrollers
  • sensors, actuators and motors
• Explore how computational thinking can be integrated into the design and development of an intelligent system, including:
  • decomposition
  • pattern recognition
  • abstraction
  • algorithms
• Communicate the logical processes performed by an intelligent system by using flowcharts, data flow diagrams and infographics
• Investigate the disruptive effects of intelligent systems, including:
  • multitasking versus digital distraction
  • working differently to complete the same task
  • automation of enterprise and manufacturing processes
  • impact of artificial intelligence (AI) on employment
• Investigate social and ethical issues to be considered when developing, implementing and using intelligent systems
• Investigate current and emerging technologies associated with intelligent systems, including AI
• Explain how intelligent systems combine innovative techniques and technologies to meet the needs of enterprise

Data and intelligent systems

• Investigate the infrastructure requirements for an intelligent network in an enterprise with devices linked through an Internet of Things (IoT), including:
  • networks with servers, local and cloud storage, and end-point devices
  • communication links that enable the efficient control and flow of data
• Explore collection, type, storage, processing, application and transmission of relevant and surplus data in an intelligent IoT network
• Investigate the application of simulation, data modelling and the automation of systems in a range of enterprises, including:
  • education and training
  • business analytics
  • high-risk applications
• Explain the role of intelligent systems in surveillance, including:
  • closed circuit television (CCTV)
  • biometric scanning
  • customer loyalty schemes
  • fraud prevention
  • sniffing
  • trolling
• Explain how AI supports efficiency in an IoT network

Creating intelligent systems

• Develop a set of rules and facts that could be used within an expert system to draw conclusions
• Apply certainty factors to construct a decision tree for a proposed expert system
• Verify the sources of data used in a decision support system
• Use a flowchart to develop a knowledge base of IF-THEN rules to be used by an expert system
• Design and model an automated smart system using a range of inputs and outputs
• Implement automated processing using software
• Assess the output produced by a decision support system, including graphing to detail the success of decisions, and comparing the proposed versus the actual outputs
• Explain how expert systems contribute to the efficiency of intelligent systems, including:
  • supercomputers
  • digital assistants
  • autonomous vehicles
  • streaming services