In the context of computer science and cognitive systems, how is “artificial intelligence” best characterized? Choose the most comprehensive description that captures its goals, methods, and embodiments.

Introduction / Context:
Artificial intelligence (AI) is an interdisciplinary field concerned with building systems that perform tasks which, if done by humans, would be said to require intelligence. The term spans philosophical positions, engineering methods, and scientific inquiry into cognition and behavior. A strong exam-ready definition should encompass these multiple facets rather than focusing on a single angle like algorithms or robotics alone.

Given Data / Assumptions:

• The question asks for the most comprehensive description of AI.
• Options include embodiment of intelligence, production of intelligent-appearing outputs, and scientific study of mind via computational models.
• We assume modern, inclusive usage across research and applications.

Concept / Approach:
AI includes: (1) engineering systems that manifest intelligent behavior (planning, perception, language, learning), (2) producing outputs indistinguishable from human expert performance under defined tests, and (3) using computers as tools to model and test theories of cognition. Historically, AI has advanced through symbolic reasoning, search, knowledge representation, probabilistic inference, machine learning, and embodied robotics—each reflecting parts of options (a), (b), and (c).

Step-by-Step Solution:

Verification / Alternative check:
Standard AI textbooks describe AI as both the science and engineering of intelligent machines and a way to understand intelligence itself by building computational models. This triangulation validates selecting the inclusive option.

Why Other Options Are Wrong:

• (a) Alone omits simulation and scientific modeling aspects.
• (b) Alone reduces AI to outputs without considering mechanisms or scientific goals.
• (c) Alone frames AI only as a study tool and ignores practical systems.
• (e) Incorrect, since a comprehensive statement exists.

Common Pitfalls:
Reducing AI to any one subfield (e.g., only machine learning or only robotics); ignoring the role of evaluation criteria and cognitive modeling; conflating intelligence with mere automation.

Final Answer:
All of the above