Master BCS AIF Exam with Reliable Practice Questions

Correct : D

Agent Terminology

Performance Measure of Agent It is the criteria, which determines how successful an agent is.

Behavior of Agent It is the action that agent performs after any given sequence of percepts.

Percept It is agent's perceptual inputs at a given instance.

Percept Sequence It is the history of all that an agent has perceived till date.

Agent Function It is a map from the precept sequence to an action.

https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_agents_and_environments.htm

An AI agent relies on its perceptual input, which is referred to as the agent's percept. This is the data that the agent collects through its sensors about its environment. The percept allows the agent to make decisions and take actions based on its environment. The agent's percept is important for Artificial Intelligence systems to be able to operate effectively. References:

[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, 'Reinforcement Learning', p.96-97. [2] APMG-International.com, 'Foundations of Artificial Intelligence' [3] EXIN.com, 'Foundations of Artificial Intelligence'

Correct : B

An agent based model is a simulation of autonomous agents (individual and collective). Machine learning can be used to learn from the data generated by the simulations. Machine learning algorithms can analyze the data generated by simulations and identify patterns, which can then be used to help the agent make decisions and take actions. References:

[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, 'Simulation and Modelling', p.101-104. [2] APMG-International.com, 'Foundations of Artificial Intelligence' [3] EXIN.com, 'Foundations of Artificial Intelligence'

Correct : D

https://griffinshare.fontbonne.edu/cgi/viewcontent.cgi?article=1008&context=ijds

Computer scientists and economists use the term 'utility' to describe how happy an agent is. Utility is a measure of satisfaction or preference, and it is used to evaluate an agent's satisfaction with a particular outcome. Utility can be used to determine the optimal decision or action for an agent to take in order to maximize its satisfaction. References:

[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, 'Decision Making and Planning', p.99-100. [2] APMG-International.com, 'Foundations of Artificial Intelligence' [3] EXIN.com, 'Foundations of Artificial Intelligence'

Correct : D

Machine learning can be used to make robots autonomous by allowing them to learn from sensor data and plan how to carry out a task. This involves using algorithms to analyze data from sensors and use this data to make decisions and take actions. By using machine learning, robots can learn from their environment and become more autonomous. References:

[1] BCS Foundation Certificate In Artificial Intelligence Study Guide, 'Robotics', p.98. [2] APMG-International.com, 'Foundations of Artificial Intelligence' [3] EXIN.com, 'Foundations of Artificial Intelligence'

Correct : D

Weak Learner: Colloquially, a model that performs slightly better than a naive model.

More formally, the notion has been generalized to multi-class classification and has a different meaning beyond better than 50 percent accuracy.

For binary classification, it is well known that the exact requirement for weak learners is to be better than random guess. [...] Notice that requiring base learners to be better than random guess is too weak for multi-class problems, yet requiring better than 50% accuracy is too stringent.

--- Page 46,Ensemble Methods, 2012.

It is based on formal computational learning theory that proposes a class of learning methods that possess weakly learnability, meaning that they perform better than random guessing. Weak learnability is proposed as a simplification of the more desirable strong learnability, where a learnable achieved arbitrary good classification accuracy.

A weaker model of learnability, called weak learnability, drops the requirement that the learner be able to achieve arbitrarily high accuracy; a weak learning algorithm needs only output an hypothesis that performs slightly better (by an inverse polynomial) than random guessing.

---The Strength of Weak Learnability, 1990.

It is a useful concept as it is often used to describe the capabilities of contributing members of ensemble learning algorithms. For example, sometimes members of a bootstrap aggregation are referred to as weak learners as opposed to strong, at least in the colloquial meaning of the term.

More specifically, weak learners are the basis for the boosting class of ensemble learning algorithms.

The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners.

https://machinelearningmastery.com/strong-learners-vs-weak-learners-for-ensemble-learning/

The best technique to adopt when a weak learner's hypothesis accuracy is only slightly better than 50% is boosting. Boosting is an ensemble learning technique that combines multiple weak learners (i.e., models with a low accuracy) to create a more powerful model. Boosting works by iteratively learning a series of weak learners, each of which is slightly better than random guessing. The output of each weak learner is then combined to form a more accurate model. Boosting is a powerful technique that has been proven to improve the accuracy of a wide range of machine learning tasks. For more information, please see the BCS Foundation Certificate In Artificial Intelligence Study Guide or the resources listed above.