Intelligent Agents Assignment Brief
| Qualification | OTHM Level 7 Diploma in Artificial Intelligence (610/4802/1) |
| Unit Reference Code | K/651/3600 |
| Unit Name | Intelligent Agents |
| Credit | 20 |
| GLH | 100 |
| TQT | 200 |
| Mandatory / Optional | Mandatory |
| Unit Grading Type | Pass / Fail |
Assignment Aim
This unit provides a broad introduction to the rapidly expanding field of agent-based computing. Learners will explore the key concepts and models involved in developing individual intelligent agents and their interactions in a multi-agent environment. A strong focus in this unit is placed on rational decision-making under uncertainty, automated negotiation, cooperation, and competitive behaviour in computational markets such as online auctions. Learners will gain practical experience by programming a trading agent in Python, which will compete in a class tournament within a simulated trading environment. The unit may also touch upon Large Language Models (LLMs) as agents to understand their growing role in intelligent systems.
Learning Outcomes and Assessment Criteria
| Learning Outcome – The learner will: | Assessment Criteria – The learner can: |
| 1. Understand the foundational principles of agent-based computing. | 1.1 Describe the key motivations for agent-based computing. 1.2 Explain symbolic, reactive, and practical models of reasoning in intelligent agents. 1.3 Critically analyse the role of rational decision making in agent systems. 1.4 Critically evaluate agent-based models for solving complex problems. |
| 2. Understand interactions between agents in multi-agent environments. | 2.1 Describe models of cooperation in agent systems. 2.2 Explain competitive behaviours in multi-agent environments using game theory. 2.3 Critically analyse the role of computational markets and auctions in agent-based interactions. 2.4 Evaluate automated negotiation models in agent systems. |
| 3. Be able to design and implement intelligent agents. | 3.1 Develop structured models of agents in code. 3.2 Implement agents in a simulated trading environment. 3.3 Apply practical reasoning strategies in agent-based computational markets. 3.4 Critically evaluate the performance of agents in competitive settings. |
| 4. Understand advanced applications and ethical considerations in agent-based computing. | 4.1 Describe advanced agent systems used in complex environments. 4.2 Analyse the effectiveness of intelligent agents in various industries. 4.3 Evaluate the ethical considerations related to deploying autonomous agents 4.4 Determine improvements for implementing agent-based systems in real-world environments. |
Assessment
To achieve a ‘pass’ for this unit, learners must provide evidence to demonstrate that they have fulfilled all the learning outcomes and meet the standards specified by all assessment criteria.
| Learning Outcomes to be met | Assessment Criteria to be covered | Assessment type | Word count (approx. length) |
| LO1-LO3 | All AC’s under LO1 – LO3 | Trading Agent Programming and Class Tournament | Python-based project + Performance evaluation (1000-word report) |
| LO4 | All ACs under LO4 | Final Coursework (Essay + Reflection) | 3500 words |
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