What are artificial intelligence career domains?
A career in this can be realized within a variety of settings including : private companies public organizations education the arts healthcare facilities government agencies and the military.
Output on ES has
The explanation may appear in the following forms −Natural language displayed on screen.Verbal narrations in natural language.Listing of rule numbers displayed on the screen.
use different Elasticsearch outputs, each one with a different value for the index parameter use one Elasticsearch output and use the dynamic variable substitution for the index parameter
User interface in expert system
User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence.
A user interface is the method by which the expert system interacts with a user. These can be through dialog boxes, command prompts, forms, or other input methods. Some expert systems interact with other computer applications, and do not interact directly with a human.
With this strategy, an expert system finds out the answer to the question, “Why this happened?” .On the basis of what has already happened, the Inference Engine tries to find out which conditions could have happened in the past for this result. This strategy is followed for finding out cause or reason.
Backward chaining is the logical process of inferring unknown truths from known conclusions by moving backward from a solution to determine the initial conditions and rules. Backward chaining is often applied in artificial intelligence (AI) and may be used along with its counterpart, forward chaining.
It is a strategy of an expert system to answer the question, “What can happen next?” Here, the Inference Engine follows the chain of conditions and derivations and finally deduces the outcome.
Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems.
In case of rule based ES
The rule based ES(expert System)Applies rules repeatedly to the facts, which are obtained from earlier rule application.Adds new knowledge into the knowledge base if required.Resolves rules conflict when multiple rules are applicable to a particular case.
Rule-based systems (also known as production systems or expert systems) are the simplest form of artificial intelligence. ... The definitions of rule-based system depend almost entirely on expert systems, which are system that mimic the reasoning of human expert in solving a knowledge intensive problem.
Inference Engine in ES
Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution.In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.
In the field of artificial intelligence, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
Knowledge Engineers in ES
The knowledge base is formed by readings from various experts, scholars, and the Knowledge Engineers. The knowledge engineer is a person with the qualities of empathy, quick learning, and case analyzing skills.
A knowledge engineer is an expert in AI language and knowledge representation who investigates a particular problem domain, determines important concepts, and creates correct and efficient representations of the objects and relations in the domain.
Knowledge Acquisition In ES
The success of any expert system majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base.
Knowledge acquisition refers to the process of extracting, structuring, and organizing domain knowledge from domain experts into a program. ... Capturing domain knowledge of a problem domain is the first step in building an expert system.
It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.
Knowledge Representation in AI describes the representation of knowledge. Basically, it is a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning.