🚀 Future ICT Trends Simulator (Competency 13)
[cite_start]Intelligent Computing, Software Agents, and Beyond von-Neumann Models [cite: 37, 36]
13.1 Intelligent & Emotional Computing
Key Concepts (13.1)
-
[cite_start]
- **Intelligent Computing:** Systems that can learn, reason, and solve problems[cite: 37]. [cite_start]
- **Emotional Computing (Affective Computing):** Systems that recognize, interpret, and simulate human emotions[cite: 37]. [cite_start]
- **Coexistence:** Man-Machine Coexistence (human-AI teamwork) and Machine-to-Machine Coexistence (AI systems communicating)[cite: 37].
Drag & Drop: Concept Classification
Drag each concept to the cell that best matches its primary capability and interaction type.
| High Reasoning / Learning | High Emotional Recognition | |
|---|---|---|
| Man-Machine Coexistence (H-M) | ||
| Machine-to-Machine Coexistence (M2M) |
Man-Machine Coexistence Scenario
Select **all** scenarios that demonstrate **Man-Machine Coexistence** (direct collaboration):
13.2 Software Agents & Multi-Agent Systems
Key Concepts (13.2)
-
[cite_start]
- **Software Agents:** Autonomous programs performing tasks on behalf of users[cite: 37]. [cite_start]
- **Characteristics:** Agents possess autonomy, reactivity, pro-activeness, and social ability[cite: 37]. [cite_start]
- **Multi-Agent Systems (MAS):** Multiple agents interacting to solve complex, distributed problems[cite: 37].
Match Agent Characteristics to Action
Click on a Characteristic, then click on the corresponding Action.
Characteristics
Action Examples
Identify Multi-Agent System (MAS) Applications
Select the most appropriate agent system application type for each scenario:
Scenario 1: An online auction platform where buyer and seller agents automatically set and adjust bids.
Scenario 2: A corporate system coordinating document review, approval, and publishing among different departments.
13.3 Beyond von-Neumann Computing Models
Alternative Computing Paradigms (13.3)
Traditional computers rely on the **Von-Neumann Architecture** (stored program, sequential). Future models explore different physical and computational bases.
-
[cite_start]
- **Nature-Inspired / Biology-Inspired:** Computing based on natural processes (e.g., genetic algorithms, DNA computing, neural networks)[cite: 37]. [cite_start]
- **Quantum Computing:** Uses **qubits** and principles like superposition and entanglement for massively parallel computation[cite: 37].
Compare Computing Models (Select Match)
Select the correct feature for each computing model:
| Feature | Von-Neumann (Traditional) | Quantum Computing | Biology-Inspired (DNA/Neural) |
|---|---|---|---|
| Processing Type | |||
| Best For |
Order Computing Models Chronologically
Drag to arrange the concepts from **oldest** to **newest** by conceptualization/common use:
Review & Assessment
Lesson Summary
13.1: Intelligent Computing
AI systems learn, reason, and coexist with humans (Man-Machine, M2M). [cite_start]Emotional Computing focuses on recognizing human affect[cite: 37].
13.2: Software Agents
[cite_start]Autonomous programs with autonomy, reactivity, pro-activeness, and social ability used in MAS for complex tasks (E-comm, Workflow)[cite: 37].
13.3: Future Computing
[cite_start]Beyond sequential von-Neumann models: Massively parallel Quantum and evolutionary Nature/Biology-Inspired computing[cite: 37].
Final Knowledge Check (3 Questions)
1. Which characteristic of a software agent allows it to negotiate a price with another agent?
2. The core principle of Quantum Computing that allows it to perform multiple calculations simultaneously is:
3. A system that analyzes a customer's voice tone to detect frustration and automatically transfer the call to a human manager uses:
Lesson Complete!
You've explored new trends and future directions in ICT. Review your progress below:
Copyright © 2026 | schoolict.net | Admin (rmssd2000@yahoo.com)