Scientists do not operate with 100 percent certainty. Findings are based on probabilities. New evidence can invalidate predictions and even modify well-accepted understandings. In many respects, uncertainty is critical for science because it spurs scientists to engage in further investigation and research.

Our brains perceive ambiguity as a threat, and they try to protect us by diminishing our ability to focus on anything other than creating certainty. Research shows that job uncertainty, for example, tends to take a more significant toll on our health than actually losing our job.

Measurement uncertainty is critical to risk assessment and decision making. Organizations make decisions every day based on reports containing quantitative measurement data. If measurement results are not accurate, then decision risks increase. Selecting the wrong suppliers, could result in poor product quality.

What is Uncertainty?

1. Uncertainty simply means the lack of certainty or sureness of an event.
2. It is not uncommon to find people who get confused between risk and uncertainty.
3. Uncertainty is one concept in finance and accounting that should be deeply understood.

When talking about Artificial Intelligence, an agent faces uncertainty in decision making when it tries to perceive the environment for information. Because of this, the agent gets wrong or incomplete data which can affect the results drawn by the agent.

There are two major different types of uncertainty in deep learning: epistemic uncertainty and aleatoric uncertainty. Epistemic uncertainty describes what the model does not know because training data was not appropriate. Epistemic uncertainty is due to limited data and knowledge.

Uncertainty: With this knowledge representation, we might write A→B, which means if A is true then B is true, but consider a situation where we are not sure about whether A is true or not then we cannot express this statement, this situation is called uncertainty.

In a reasoning system, there are several types of uncertainty. Reasoning under uncertainty research in AI is focused on uncertainty of truth value,in order to find the values other than True and False.

A belief network defines a factorization of the joint probability distribution, where the conditional probabilities form factors that are multiplied together. A belief network, also called a Bayesian network, is an acyclic directed graph (DAG), where the nodes are random variables.

TMS are another form of knowledge representation which is best visualized in terms of graphs. It stores the latest truth value of any predicate. The system is developed with the idea that truthfulness of a predicate can change with time, as new knowledge is added or exiting knowledge is updated.

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Simple relational knowledge: It is the simplest way of storing facts which uses the relational method, and each fact about a set of the object is set out systematically in columns. This approach of knowledge representation is famous in database systems where the relationship between different entities is represented.

Advantages of Expert System Reduces the cost of consulting an expert for solving the problem.

The fundamental goal of Knowledge Representation is to facilitate inferencing (conclusions) from knowledge. The fundamental goal of Knowledge Representation is to facilitate inferencing (conclusions) from knowledge. The issues that arise while using KR techniques are many.

There are two types of inferences, inductive and deductive. Inductive inferences start with an observation and expand into a general conclusion or theory.

Explanation: An agent can improve its performance by storing its previous actions.

Issues in Knowledge Representation

• Important Attributed: Any attribute of objects so basic that they occur in almost every problem domain?
• Relationship among attributes: Any important relationship that exists among object attributed?
• Choosing Granularity:
• Set of objects:
• Finding Right structure:

Here are the four fundamental types of knowledge representation techniques:

• Logical Representation. Knowledge and logical reasoning play a huge role in artificial intelligence.
• Semantic Network.
• Frame Representation.
• Production Rules.

Approaches to knowledge representation simple relational knowledge, inheritable knowledge, inferential knowledge, and procedural knowledge—each of these ways corresponding to a technique of representing knowledge discussed above.

“A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph.” It is also called a Bayes network, belief network, decision network, or Bayesian model.