Including lower computing costs and easier implementation

1. One definition of knowledge representation is "the act of representing knowledge in a manner that a machine can understand and alter." Knowledge representation is essential for building a KBS since it gives the machine the ability to reason about the domain.

2. Knowledge-based system software that makes inferences and decisions based on the rules and facts stored in the knowledge base. The inference engine processes the information by applying the relevant rules and data.

7. Knowledge engineering, number seven on the list, is the method of developing and implementing a knowledge-based system. Knowledge engineers often do tasks like data mining, knowledge modeling, and inference engine development.

8. Heuristics, which are suggestions or rules of thumb used to solve problems or make decisions, are discussed in the eighth paragraph. Heuristics are widely used to improve the rules and facts stored in a knowledge base in knowledge-based systems.

Define what is meant, with regards to Computer Science, by the term ‘a Knowledge-Based System’ and describe the main features and possible architectures of such a system. By reporting on the processes that could be followed and suggesting how a Knowledge Based System (KBS) might be developed? Considering Computational Agents as a KBS, propose an architectural design for a simple reactive agent and show how this can be evolved to account for more complex ‘intelligent’ agents. Fully describe the many types of environment that the agent will need to interact with and the properties of various agent types.

Knowledge-based systems (KBSs) are a subset of AI that employs a database of information to make decisions and address problems. A KBS attempts to mimic a human expert's level of judgment and decision-making in a certain domain, like medicine, engineering, or finance.

The capacity to tap into the expertise of human domain experts is a cornerstone of KBS. Having this data encoded in a machine-readable way allows the KBS to provide consistent and reliable recommendations or answers. The usage of KBSs can increase efficiency and cut down on errors by automating mundane tasks that formerly required human intervention.

The Main features KBS:

4. Explanation Module: The Explanation Module is a crucial aspect of any KBS since it explains the thought process that led to the system's conclusion or recommendation. It may be adjusted to provide several levels of detail, such as a high-level summary of the decision-making process or a detailed explanation of the reasoning behind the choice. It can also highlight any discrepant data and provide details on the system's certainty or confidence in its conclusion. The Explanation Module may be modified to respond to a variety of user actions, such as inquiring for further details or changing settings. Critical to the success of a KBS, it inspires trust in the system and enables users to make educated choices in light of the KBS's recommendations.

5. Learning Module: The purpose of the Learning Module in a Knowledge-Based System (KBS) is to allow the system to learn and improve its performance over time. As it enables the system to learn from experience, fix mistakes, and make more informed choices, it is a crucial part of any intelligent system. Depending on the nature of the available data and the nature of the problem at hand, many methods can be used to construct a Learning Module in a KBS.

In most cases, the Learning Module will have the following sub-modules:

• Data Preprocessing: While building a knowledge base, a Knowledge Base System (KBS) must first go through the steps of data preparation. Inspection of raw data for errors, inconsistencies, and gaps, followed by correction. The term "data transformation" refers to the process through which unprocessed data is converted into a form suitable for further study. Data preparation techniques in KBS include dimensionality reduction, feature selection, feature scaling, and outlier identification and removal. In order to ensure that a KBS has accurate and actionable information, data preparation is a crucial step in the KBS development process. KBS developers may improve the system's efficiency and ensure it reliably generates precise results by making use of appropriate data preparation strategies.

Ultimately, the Learning Module is an important part of a KBS since it allows the system to learn from its mistakes and grow as a result.

6. Knowledge Acquisition Module: A KBS's Knowledge Acquisition Module is responsible for collecting data from external sources and adding it to the system's current knowledge base. Knowledge elicitation, information extraction, knowledge fusion, and knowledge engineering are all examples of popular learning methodologies. Knowledge acquisition is crucial since a KBS requires accurate and relevant data to make sound decisions. The quality of the data collected by the module is crucial to the achievement of the KBS's goals.

1. In rule-based systems, Knowledge representation and reasoning systems, rule-based systems (or production systems) follow a specified set of rules to draw inferences and decisions. As the rules capture domain-specific knowledge, they act as the knowledge foundation. Every rule consists of two parts: the premise and the response. When a new problem or piece of information is presented to the system, it searches through its rules until it finds one that works. Because of their intuitiveness and adaptability, rule-based systems make reasoning transparent and verifiable. Nonetheless, they could struggle with information that is obscure or complex. In these cases, alternatives like fuzzy logic and probabilistic reasoning might prove effective.

2. In frame-based systems, KBS architecture makes use of frame-based systems (KBS). Frames are models for describing things and ideas in terms of their attributes, values, and relationships to one another. Several aspects of the object or concept being displayed can be represented via slots in the frame. Features like as inheritance, default values, and slot limits make frames a more flexible manner of knowledge representation. A frame-based KBS consists of four main parts: the knowledge base, the inference engine, the knowledge acquisition module, and the knowledge acquisition module. The knowledge base stores the frames that characterize the domain knowledge, while the inference engine is responsible for deducing from the data in the knowledge base.

The choice of architecture is influenced by the nature of the issue, the information available, and the required performance standards. Each design has its own accuracy, efficiency, scalability, and interpretability tradeoffs.

Developing a knowledge-based system:

The development of a basic reactive agent into a complex "intelligent" creature may be understood by looking at the opposite extreme.

A computational agent with a limited set of rules for responding to its environment is called a basic reactive agent. A thermostat that kicks on the heater when the temperature drops below a certain point and turns it off when it rises above another one is one such device.

For instance, a smart thermostat may analyze past heating and cooling trends to optimize them in light of current weather predictions, occupancy levels, and energy costs. The thermostat may apply reinforcement learning to fine-tune its reward function, which may be affected by factors such as user satisfaction, energy savings, and environmental impact.A computational agent, or other Knowledge-Based System, may be developed by the collection of relevant data, its proper representation, and the application of problem-solving and deductive reasoning algorithms. Adding more advanced reasoning and learning mechanisms to a simple reactive agent can help it develop into a more intelligent agent. Improved decision-making and problem-solving in challenging and ever-changing scenarios are possible outcomes of using such methods to help the agent set goals, create strategies, and learn from experience.

An agent is a program or piece of hardware in the field of artificial intelligence that can gather information about its environment via sensors and then use that data to take some sort of action. The environment can take several forms depending on the use case, and some agents may function better in certain conditions than others. The many agent kinds and the types of environments they would need to deal with are summarized below.

Control systems for industrial processes are good examples of deterministic systems because their behavior must always be precisely as expected. In a random system, the outcomes can be unpredictable to some extent. Coding and debugging deterministic KBSs is easier, but random KBSs are more flexible. Although random KBSs are more flexible and responsive to changing conditions, deterministic KBSs are simpler to code and debug. Which KBS is best to use is determined by the nature of the application and the desired qualities of the end result.

3. Episodic and Sequential:

A system in a discrete environment is limited to a small set of fixed conditions. KBSs use methods like rule-based reasoning, decision trees, search algorithms, and reinforcement learning to determine the optimal next step to take in each state. For instance, the board position of the pieces, the number of moves made by each player, and the availability of specific moves are all examples of discrete variables that can be represented in the KBS chess program, which is an example of a discrete environment. As another application of KBS in a discrete setting, robot controllers model the world around them as a set of binary states, such as the robot's current location and the presence of obstacles. Due to the well-defined and limited state and action spaces, KBSs typically have simple methods and data structures to implement, which has many benefits, including lower computing costs and easier implementation. It's possible, though, that KBSs built with neural networks and deep learning would be the most helpful in such a scenario.

Finally, a one-of-a-kind ecosystem In KBS, the system is restricted to a small number of states and operations that have been carefully mapped out. These systems are frequently used in robotics and gaming and can be built using a variety of methods including rule-based reasoning, decision trees, and search algorithms. However, they may not be the best choice when dealing with persistent or intricate issues, despite their many advantages.

Several architectures, such as rule-based systems, decision trees, and search algorithms, can be used to create them. Nevertheless, they have limits, such as requiring a clear and well-defined set of objectives, and may struggle in complicated and unpredictable contexts in which objectives are not well-defined or are continuously changing.

A utility-based agent is a form of agent that makes decisions based on a utility function. This function converts the state of the environment and the agent's possible actions into a numeric number representing the attractiveness of that condition or action. A utility-based agent's objective is to maximize its anticipated utility, which is the total of the utilities of all conceivable events weighted by their probabilities of happening. They are especially valuable in decision-making tasks involving trade-offs between several outcomes. Nevertheless, utility-based agents have drawbacks, including the need for a well-defined and precise utility function and difficulty in contexts with complicated and unpredictable dynamics. In cases when there is a large degree of uncertainty or risk, optimizing for predicted utility may not always be the optimal strategy.