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- One of the most important aspects of agents is the extent to which they have incorporated intelligence. When it comes to cognitive talents, intelligence encompasses a broad range of skills that may be categorised by phrases such as "observing," "recognising," "deciding," "planning," "problem-solving," and "learning." When it comes to agents, intelligence may be defined as the way they do their responsibilities successfully. To derive judgments for one's own active actions in terms of the target, one must interpret task-related knowledge and recently acquired information in a way that is relevant to the tasks at hand.
- According to the job and functional capabilities of the agent, the intelligence of the agent might vary greatly. As a result, there is a broad variation of performance amongst agents. Artificial intelligence is exhibited through programmes that display cognitive properties, which are the embodiment of artificial intelligence. These must be implemented via the use of a computer platform to be effective. As a result, one may also talk about artificial intelligence. The implementation may be carried out on a single computer or, in certain cases, on a computer network as well.
- The necessary control and regulatory functions are then executed on this platform. The need for such computers to be supplemented with a process and communication interface has previously been identified. Intelligent behaviour is founded on the possession of task-related knowledge, which is a more complex kind of information than general knowledge.
- A metaphor for this is used in the context of artificial intelligence (AI) or artificial intelligence (AI), which is defined by the existence of a knowledge base as well as a conclusion component (inference mechanism) in the system. In the knowledge base, many types of existing system knowledge may be stored in a variety of ways (e.g., production rules, frames, constraints). In rule-based systems, information is represented as facts and (production) rules, which are stored in a knowledge base and may be accessed by programmers.
- This information must be brought into the intelligent system from the outside (for example, by consulting experts) via the use of a knowledge acquisition component, but it may also be gained, updated, or extended through the process of learning. Knowledge processing determines the appropriateness of replies generated by the reasoning component, which in turn is decided by the knowledge processing component. In this instance, the expenditures are related to the activities that will be conducted. Neural networks are another important kind of intelligent system that must be considered.
- These are adaptive networks that were created using basic brain components as a starting point. In this situation, the information is dispersed among the neurons of these networks and stored because of this. There are additional methods to intelligent systems that are entirely reliant on agents. The intelligence services do not provide any of these capabilities currently. Adaptive behaviour is a transitional kind of behaviour. Agents that possess this ability can adapt to situations that change slowly, such as changes in their surroundings. (Machine) learning is a more advanced kind of intelligence. This refers to the capacity to analyse experiences received via conversation with the environment or with the user to acquire new, task-related information or to alter current knowledge to complete the task.
- Knowledge-based systems are a research object with which the artificial intelligence (AI) as a scientific field is collaborating on a scientific project. Computer algorithms that, in addition to information, also include heuristics and uncertainty contain subjective knowledge are referred to as artificial intelligence (AI). In particular, the systematic inventory of expert knowledge necessitates the processing of poorly organised knowledge categories from the outset of the inventorying process. A schematization and abstraction of expert information occurs as a result, and this may be used to detect knowledge gaps, with education contributing to the development of new models and theories because of this process.
- This is strongly related to the construction of structures to codify and programme the obtained data to carry out knowledge in a subsequent stage. The importance of knowledge-based systems does not follow just because they include subjective knowledge; rather, they distinguish themselves from other artificial intelligence and traditional programmes by virtue of their own separation and explicit representation. Applicant particular information should be used clearly and independently from other system-wide information.
- It is possible to make information accessible by making it available, and there are ways for the methodical processing of different fields of knowledge. This implies that knowledge-based systems are not merely for those who aren't experts in their fields. It is possible to develop and alter the knowledge base by adding, amending, or removing knowledge structures in accordance with the Knowledge may be represented in a variety of ways by the knowledge acquisition component. It is quite often referred to as homogenous, and when it is not, it is referred to as heterogeneous. The knowledge base holds the information that may be used to solve an issue in the future. The goal is to prevent the less competent user from learning anything.
- Five components are the fundamental components in the literature. Many systems do not have an integrated acquisition component to detect a change in the data contained in the database, while typical programmes do so by separating the information stored in the knowledge base from the techniques for processing that knowledge. You have one in his area of expertise, and you possess one in the examination and processing of that subject. knowledge that has been captured in structures that can be processed by the programme an addressed issue is triggered by a condition during the problem-solving process, facts are used.
- The knowledge acquisition component provides for the creation and modification of the knowledge base via the addition, modification, and deletion of knowledge structures in accordance with the representation of knowledge established for the system by the system's designers. The tools available for this may vary from a basic text editor for creating source code to more complicated ones that are developed input tools are suitable for the task at hand. These translate, for example, recorded information in tabular form into structures that can be handled by the software, such as a database. Many systems do not include an integrated acquisition component to detect a change in the environment to prevent knowledge by the less qualified user from being gained.
- The knowledge base holds the information that may be used to solve an issue in the future. Various structures may be used to represent different types of knowledge. Production rules and facts are often used in this context, and their programming and processing by suitable programming languages, such as, for example, B. PROLOG, special, is supported. If the information is kept in the knowledge base in the form of uniform structures, it is referred to as homogeneous knowledge; otherwise, it is referred to as heterogeneous knowledge. The inference component is responsible for processing the previously stored information. Certain methods (inference mechanisms) that may be utilised to discover a solution for a user-defined problem are included in the code that is created.
- Facts that are inferred throughout the solution process are also kept in the knowledge base, which might be either permanent or transient in nature. Together with the knowledge base, the inference component is at the heart of a work breakdown structure. The dialogue component oversees controlling the communication between the system and the user. Your primary responsibility is to process the system-internal representations of information for the user in such a way that it seems intelligible, and communication occurs in a format that is suitable for the user's ability. Additional to providing explicit user assistance and processing outcomes from the solution process, the dialogue component is also responsible for sending system inquiries to the user.
- To guarantee that prompts are understandable by the user, the It seems logical to combine the dialogue component with an explanation component in this case. However, the explanation component's responsibilities do not end with the rendering of Help messages for the purpose of user guiding. The use of unique protocol functions allows it to provide justification for inquiries and outcomes. User requests are, for example, more transparent when the reason for the request is explained. When results are presented, they should be comprehended in the same way that they were inferred.
- The latter feature is very important for the usage and adoption of a system, such as B. as a learning programme, and should not be undervalued in this regard. It is becoming less necessary to provide a thorough description of how to discover a solution as the user's knowledge increases. As a result, an explanatory component should be created such that the severity of the guidance may be varied over time. The inference component is responsible for processing knowledge. Inference is the process of deriving inferences from information that has been provided. Broadly speaking, inference is a computing technique that applies previously acquired information to data structures with the purpose of achieving believable outcomes.
- The reciprocal search for variables through comparing patterns ("pattern matching") and recording ("instantizing") have both been shown to be essential control mechanisms in the reasoning processes involved. A virtual dynamic database is created after the data or goal of the reasoning process is specified. Appropriate rules are identified through "pattern matching," which is the comparison of rules from a knowledge base with elements from the working memory. Free variables are instantiated and stored as data in the virtual dynamic database. Now, the rules of the knowledge base are being compared with the most recent material of the dynamic database, which is being updated. This method is done until a rule is fulfilled or until no longer feasible pattern matching can be performed.
When it comes to knowledge management, the importance of intellectual capital, the globalisation backdrop, the rising complexity of our world, and the necessity for "smart" businesses are all often cited as reasons for its use (Kohout et al., 2019). We like to approach knowledge management from the beginning, from the source of the knowledge, which is an intangible entity that causes challenges for businesses. Originally, the goal of knowledge management was to preserve and develop know-how, or any other kind of information. It is founded on principles and is responsible for a variety of tasks inside the company. As a rewarding and distinguishing feature from others, we refer to knowledge-based businesses as well as knowledge-based individuals (competitors or employees).
Know-how is the capacity to build a firm, and it is not equal to the total of the individual know-how of the individuals who make up the organisation. The organization's know-how is complicated, and it is also dependent on the relationships that exist between the players in the organisation. Quality management techniques attempt to improve awareness of the complexity of the organisation via their assessments by identifying the connections of influence that have an impact on or contribute to performance development (Alor-Hernández & Valencia-García, 2017). The comprehensive strategy used by the corporation is in response to the rising complexity of the corporate environment.
The know-how that we are interested in is that of the individuals who are involved in the organisation. It is inextricably linked to the one who has it. The interchangeability of competence is a concept that corporations have been seeking to establish in recent years by rationalising talents in terms of observable and controllable features (i.e., making them an external resource to people). However, certain individuals have a unique talent, efficiency, and optimum knowledge, which makes them persons who are tough to exchange with other people.
When a knowledge bearer goes on vacation or, more decisively, when he or she resigns or retires, the significance of that knowledge is often assessed. This "leakage" of knowledge has the potential to have a greater or lesser impact on the activity. It takes time to "re-manufacture" the latter, since it is the consequence of experiences, instances met, empirical approaches, optimization of gestures and ways of doing things, among other factors. There are several instances of successful businesses throughout history (Bi et al., 2017). However, in other situations, such as sales operations carried out by a group of workers, individual performance varies from one employee to the next.
These discrepancies may be explained by the situation (a challenging location, for example), but they can also be explained by a disparity in knowledge (we also speak of competence). Shared knowledge of top selling does not always transform everyone else into excellent sellers, but it certainly contributes to improved collective performance. The management of know-how and knowledge, in general, is one of the last levers of progress treated in the race for optimization: we have mechanised, organised, automated, and robotized the process of progress; we have managed human resources, skills, and now know-how and knowledge in general; and we have managed knowledge in general. The term "know-how" refers to a specific subset of knowledge. Because of its "volatile" nature, it is essential for specific activities, but it is just a subset of all activities in general. Enthusiasts for "knowledge management" need just move one step farther to develop an interest in the remainder of the subject matter: knowledge.
Afterwards, the term "knowledge" is utilised in all its potential meanings, even going so far as to pass material off as knowledge and messaging software as a knowledge management tool. According to the information presented in the chapter "Knowledge," knowledge is a highly definite "thing." Individuals and their knowledge are the primary emphasis of this section. What information should be imparted to corporate personnel is an issue that emerges for the organisation. What information will we need tomorrow? How does one go about acquiring this knowledge? These are issues that emerge in the short term (right now) and in the medium term (in the future) (tomorrow). Companies recognise that they must know what they are doing, and although in the past we learned a profession for life, technology, methods, techniques, trends, and so on are changing at an ever-increasing rate (Bi et al., 2017).
If a corporation must adapt and learn (response), it must also avoid and prepare for the changes that will occur in the future (pro-action), as well as attempt to separate itself from the competition (innovation). Organization of the flow of information to and from the firm's personnel occurs daily at the company (continuous training, information, exchanges, etc.). From a strategic standpoint, it imagines and defines itself to accomplish its goals, and it projects itself far into the future to achieve those goals. To reach these, she periodically examines her organisation and, when she has the opportunity (which is sadly still uncommon), considers the knowledge she will need to build or acquire.
And then waiting for the anthill to rebalance itself by weighting with all its management weight. Many managers, on the other hand, believed that it was impossible to codify "everything in a person's mind" in 1995, when "knowledge management" started to become a new popular topic of discussion. The brain continues to be riddled with riddles, and knowledge management does not pretend to have all the answers. In the end, to the managers' credit, the "KM" did not exist "before," and businesses have always been able to operate well. Corporations were unwittingly engaging in knowledge management activities via companionship, informal exchanges, recruiting, and training without realising it. Technology (such as formalisation of knowledge, artificial intelligence, and communication) and therefore new possibilities are brought about through knowledge management initiatives. It is concerned with the processes of the company, identifies "essential" know-how, and facilitates the implementation of strategic knowledge management initiatives.