Intelligent Systems

9 dot points across 3 inquiry questions, in syllabus order. Each dot point has a focused answer, with exam-style questions and worked answers where available.

Systems and their applications

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 and 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

The disruptive effects of intelligent systems (multitasking versus digital distraction, new ways of working, automation, AI and employment), the social and ethical issues in developing and using them (bias, transparency, accountability, privacy, safety), current and emerging AI technologies, and how enterprises combine techniques to meet their needs.

9 min read →

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) and automated decisions (structured)

What a decision support system (DSS) is, its components (data, models, user interface, users), where DSSs are used, and the three categories of decision-making in the syllabus: structured (automated), semi-structured (a finite set of instructions plus judgement) and unstructured (judgement required).

7 min read →

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 and webometrics; describe how people's changing needs have shaped the proliferation of expert systems, including the Internet of Things (IoT), Internet of Me (IoMe) and 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

Why expert systems moved from fixed rules to probability (processing power, data availability, hardware costs, neural networks, webometrics), how IoT, the Internet of Me and Industry 4.0 drove their spread, and how simple and complex intelligent agents in search engines work, including predictive search and voice assistants.

8 min read →

Investigate common applications of expert systems, including diagnosis, monitoring, process control, and scheduling and planning; describe key features of an expert system, including knowledge base, inference engine, including forward chaining and backward chaining, and user interface (UI); compare techniques used by different inference engines, including truth maintenance, hypothetical reasoning, heuristic knowledge and fuzzy logic, and ontology classification

What an expert system is and where it is used (diagnosis, monitoring, process control, scheduling and planning), its key features (knowledge base, inference engine with forward and backward chaining, user interface), and how inference techniques compare: truth maintenance, hypothetical reasoning, heuristics and fuzzy logic, and ontology classification.

9 min read →

Investigate the hardware used in an intelligent system, including biometrics, haptics, touch and gesture, virtual and augmented reality (VR/AR), voice and sound, microcontrollers, and 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 and algorithms; communicate the logical processes performed by an intelligent system by using flowcharts, data flow diagrams and infographics

The hardware intelligent systems use to sense, decide and act (biometrics, haptics, touch and gesture, VR/AR, voice and sound, microcontrollers, sensors, actuators and motors), how decomposition, pattern recognition, abstraction and algorithms guide their design, and how flowcharts, data flow diagrams and infographics communicate their logic.

8 min read →

Data and intelligent systems

Investigate the application of simulation, data modelling and the automation of systems in a range of enterprises, including education and training, business analytics and high-risk applications; explain the role of intelligent systems in surveillance, including closed circuit television (CCTV), biometric scanning, customer loyalty schemes, fraud prevention, sniffing and trolling

How enterprises apply simulation, data modelling and automation in education and training, business analytics and high-risk applications, and the role intelligent systems play in surveillance through CCTV, biometric scanning, loyalty schemes, fraud prevention, sniffing and trolling, with the benefits and ethical concerns of each.

8 min read →

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, and 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; explain how AI supports efficiency in an IoT network

The infrastructure of an enterprise IoT network (end-point devices, gateways, servers, local and cloud storage, communication links), how relevant and surplus data is collected, stored, processed, applied and transmitted, and how AI improves efficiency through edge processing, predictive maintenance, optimisation and anomaly detection.

8 min read →

Creating intelligent systems

Verify the sources of data used in a decision support 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

How to verify the data sources behind a decision support system, design and model an automated smart system from inputs to outputs, implement automated processing in software, and assess DSS output by graphing results and comparing proposed with actual outcomes.

8 min read →

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; use a flowchart to develop a knowledge base of IF-THEN rules to be used by an expert system; explain how expert systems contribute to the efficiency of intelligent systems, including supercomputers, digital assistants, autonomous vehicles and streaming services

How to build a small expert system: write facts and IF-THEN rules, turn a flowchart into a knowledge base, attach certainty factors and draw a decision tree, and explain how expert-system reasoning makes supercomputers, digital assistants, autonomous vehicles and streaming services more efficient.

9 min read →