Artificial Intelligence: Propositional Logic and First-Order Logic

This essay delves into the realm of Artificial Intelligence (AI) and Machine Learning (ML), defining intelligence as the acquisition of knowledge and ML as the acquisition of skills to enhance success. It highlights the core differences, with AI aiming for optimal solutions and ML focusing on accuracy. The paper explores various applications of AI, including medical diagnosis, remote sensing, and control systems, while ML finds applications in financial services, healthcare, and marketing. It presents examples like Siri, Tesla, and Netflix. The essay then introduces Knowledge Engineering, a field within AI that creates rules for data application, aiding in problem-solving. Furthermore, it discusses propositional logic and its limitations in representing complex statements, contrasting it with the more powerful first-order logic. The first-order logic is described as an extension of propositional logic, allowing for the representation of objects, relations, and functions, offering a more comprehensive approach to knowledge representation in AI. The essay concludes by referencing key sources on AI and machine learning.

Running head: ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE
Name of the Student
Name of the Organisation
Author Note

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Artificial Intelligence Machine Learning
In Artificial Intelligence, intelligence is
known to be actually defined acquisition of
the intelligence of knowledge which is
defined as the capability of both applying as
well as acquiring knowledge
(Veeramachaneni et al., 2016).
Machine learning is actually defined as the
specific acquisition of the skill or rather
knowledge.
The main aim is to directly incrementing the
specific chance of achieving success and not
the accuracy.
The main aim is to increment the accuracy
and it does not at all care regarding the
success.
It actually works as a program of the
computer that does work smartly.
It is basically a very simple concept where
machine takes up the data and easily learns
from that data (Jordan & Mitchell, 2015).
It actually leads to the development of the
system for mimicking human for directly
responding behave in certain circumstances.
It involves the creation of all the several
algorithms of self-learning.
AI actually goes for seeking the particular
optimal solution.
It actually goes for only the specific solution
for particularly that if it is optimal or not.
AI is being used for a broad range of a
number of activities like the medical
diagnosis, sensing of remote and controlling
of remote (Russell & Norvig, 2016).
Machine learning is directly applied to a
number of domains like that of financial
services, sales and marketing, healthcare as
well as the government.
Some of the examples of AI utilized
nowadays will be involving Siri, Tesla,
Netflix, Cogito, Flying Drones, Echo and so
Some of the examples of Machine Learning
utilized nowadays will be involving
recognition of image and speech, prediction

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on. and arbitrage which is statistical.
Knowledge engineering is actually a specific field of Artificial Intelligence or AI
which will be capable of creating a number of various rules for the application of data for
directly mitigating the specific procedure of thought of an expert of human. It particularly
looks at the specific structure of a decision for the direct identification of how any conclusion
can be reached (Alpaydin, 2016). A total library of several methods of solving problems and
the collateral knowledge can be utilised for each of them can then be easily diagnosed by the
particular system. The software which will be actually resulting can then be assisting in the
diagnosis, trouble shooting and also for solving a number of various issues wither on
particularly in its own or rather in the supporting role to an agent of human.
In the particular topic of propositional logic, it has been well noticed that how all the
several statements can be represented utilising the propositional logic (Mizoguchi &
Bourdeau, 2016). Unfortunately, in particularly the propositional logic, only all the various
facts can be well represented which will be either true or rather false. Propositional logic is
not at all sufficient for representing all the several sentences which are very much complex or
rather the statements of natural language. The propositional logic is known to be possessing
very much limited power of expressiveness. Let the following sentences be considered which
cannot be represented utilising the logic of Propositional Logic.
 Some human are very much intelligent
 Rahul likes cricket
For representing both of the above statements, the PL Logic will not be sufficient and
hence there will be requirement of some much more powerful logic like the first-order logic.

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First-order logic is some other way of the representation of knowledge in AI and it is
basically an extension to that of the propositional logic (Brown, 2014). The first order logic is
also called the first order predicate logic. It is really a very much powerful language that will
be developing information regarding all the various objects in a much easier way and can also
be expressing the specific relationship in between all of those objects. The first-order logic
does not at all assumes only the fact that world contains a number of facts like the
propositional logic but there are certain things which are also assumed. They involve objects,
relations and the function. Objects will e including A, B, numbers, colours, wars, theories and
people. Relations will be including either unary relation or rather n-any relation. Unary
relation says the red, round, is totally adjacent and n-any relation can be such like the sister
of, has colour, or rather brother of. Lastly, the function will be involving the father of, end of
and the third inning of. As a natural languages will be considered, the first-order logic is
known to be exactly comprising of two main parts which are the syntax and the semantics.

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References
Alpaydin, E. (2016). Machine learning: the new AI. MIT press.
Brown, F. M. (Ed.). (2014). The Frame Problem in Artificial Intelligence: Proceedings of the
1987 Workshop. Morgan Kaufmann.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and
prospects. Science, 349(6245), 255-260.
Mizoguchi, R., & Bourdeau, J. (2016). Using ontological engineering to overcome AI-ED
problems: Contribution, impact and perspectives. International Journal of Artificial
Intelligence in Education, 26(1), 91-106.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia;
Pearson Education Limited,.
Veeramachaneni, K., Arnaldo, I., Korrapati, V., Bassias, C., & Li, K. (2016, April). AI^ 2:
training a big data machine to defend. In 2016 IEEE 2nd International Conference on
Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on
High Performance and Smart Computing (HPSC), and IEEE International
Conference on Intelligent Data and Security (IDS) (pp. 49-54). IEEE.