What is AI representation? A Guide to What Drives AI

In a world increasingly driven by artificial intelligence (AI), there exists a powerful but often overlooked concept that serves as the backbone of this movement, AI representation. 

You probably haven’t heard of it because it is the language of machines, but it is the key that unlocks the ability to understand, interpret, and make sense of the huge amounts of data they come across.

Yet, despite its profound significance as far as AI systems are concerned, AI representation remains shrouded in obscurity for many. In this piece, we will delve into the depths of technical jargon and methodology and come out more informed with a whole new insight into the data science behind incredible AI.

Understanding AI knowledge representation

AI representation refers to the way information or knowledge is stored and organized in an artificial intelligence (AI) system. Just like how human brains use different methods to remember and understand things, AI systems also need a way to store and process information effectively. 

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Thus, it is referred to as AI knowledge representation and is done because it helps the system make sense of the data it receives and enables it to make decisions or perform tasks based on that information.

AI representation is like giving the system a set of rules or frameworks to interpret and process information. This representation can take different forms, such as symbols, numbers, and graphs, depending on the type of data and the problem the AI is trying to solve.

For example, suppose an AI system is trained to recognize objects in images. In that case, its representation might involve breaking them down into pixels and assigning values to them to describe the colors and shapes. The AI system then learns patterns using neural networks from these representations to identify objects. 

By using the right representation, an AI system can organize, analyze, and reason data more efficiently, which helps it perform tasks like image recognition, language translation, and decision-making. 

The choice of representation is crucial because it determines how well the overall system can understand and process the information it receives, ultimately influencing its performance and capabilities.

These are the knowledge types that need to be stored within an AI system: 

  • Objects: These objects can be physical, concepts, ideas, or relationships. AI knowledge representation involves capturing the properties and relationships of objects. This allows AI systems to understand and reason in the problem domain.
  • Events: Events represent actions and occurrences in our world. It involves capturing and modeling events, including what triggered them, who was there, and the consequences. 
  • Performance: AI systems benefit from representing their own knowledge and abilities. This involves encoding meta-knowledge about the AI’s knowledge, limitations, and strengths. Being self-aware helps to determine when to seek internal input and see what areas need improvement.
  • Facts: These are the true statements of the world. This involves encoding facts ranging from simple assertions to complex ideas. It allows AI systems to store and reason about objective knowledge that can be used as a foundation for decision-making.
  • Knowledge-base: A knowledge base is a database that stores the encoded knowledge and information for AI systems to access.

Types of knowledge representation

There are different kinds of knowledge representations, each tailored to different problem domains and AI techniques. Each type offers unique advantages that are suited for specific tasks and applications. 

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Declarative knowledge

Declarative knowledge refers to facts, information, and statements about the world. It focuses on capturing what is known rather than how to perform specific tasks. It is usually portrayed through symbolic representations such as logic-based languages or knowledge graphs.

The advantage of declarative knowledge is that it enables logical reasoning to draw conclusions and answer questions with inference mechanisms. This is when the AI system scans its database and applies everything it knows about logical rules to come to a conclusion and, in some cases, create new facts. 

Procedural knowledge

Procedural knowledge encompasses instructions, rules, and methods that guide the actions in a specific problem domain. AI systems can complete difficult tasks by following set procedures and algorithms. 

It can be represented using programming languages and algorithms that capture the sequence of steps needed to execute a task. This representation can include control structures, conditionals, loops, and other constructs that define the flow of operations. 

Meta knowledge

This is essentially the knowledge of knowledge. With all of this understanding about its pitfalls and strengths, AI systems can reason, learn, and make better-informed decisions to improve their performance and adaptability. Meta knowledge includes:

  • Language used for representation: Can involve information about syntax, semantics, and language rules using natural language processing (NLP). Artificial intelligence systems get much better at understanding input from the real world.
  • Structure of the knowledge base: This is the information about the organization and structure of the knowledge base, including relationships between concepts and the rules of reasoning.
  • Levels of uncertainty and confidence: The knowledge stored in AI systems must be accurate and credible.
  • Reasoning and inference mechanisms: This encompasses the knowledge of logical rules, reasoning strategies, and inference algorithms to make deductions from existing knowledge.
  • Learning and adaptation process: Involves how the system acquires new knowledge, updates existing knowledge, and adapts representation based on new experiences.
  • Limitations in the representation: Includes the understanding of certain representations being inadequate or when certain reasoning processes lead to incorrect conclusions.

Heuristic knowledge

Heuristic forms of knowledge refer to using practical strategies and rules of thumb instead of strict logic. It helps AI systems make educated guesses and approximate solutions when precise knowledge is unavailable to obtain. Heuristic knowledge guides decision-making processes, prioritizes actions, and simplifies complex problems. 

While it offers efficient problem-solving, it may not guarantee optimal methods and leaves room for biases. Thus, careful design and evaluation are important for a reliable system. 

Structural knowledge

This is the representation of relationships between different pieces of information. It captures the underlying structure and organization of knowledge, such as networks, hierarchies, and graphs. A full understanding of these relationships enables efficient navigation and retrieval of relevant information. 

Techniques of AI knowledge representation

There are various ways that AI knowledge is represented; they all play a vital part in telling the Ai system exactly what it is looking at and how to use it appropriately.

Logical representation

Logical representation serves as a foundation for various AI systems by using propositional logic, predicate logic, or first-order logic to represent knowledge.

With this particular type of representation, syntax is defined to specify the structure of logical statements, while semantics determines their meaning and truth conditions. Propositional logic represents knowledge using true or false propositions, while predicate logic introduces relationships and quantifiers. 

Rules and inference mechanisms can be made through logical representation, allowing AI systems to make logical deductions and draw conclusions based on specified conditions. Finally, it also expands knowledge bases, enabling AI agents to learn and incorporate new information into their own logical representation.

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Production rules

Production rules serve as a fundamental technique for knowledge representation within AI systems. They encode knowledge in the form of conditional statements, allowing AI agents to make decisions and take actions based on specific conditions. 

Production rules also provide flexibility, allowing for easy modification and addition of rules without putting the rest of the system out of order. This lets new conclusions be generated based on existing knowledge and provide reasoning behind certain actions. 

Frame representation

This AI knowledge technique uses frames and data structures to store information about objects and concepts. They consist of slots, representing attributes or properties, and fillers, which hold the corresponding values.

Frame representation supports inheritance, organizing frames hierarchically and inheriting attributes from higher-level frames. Default values can be defined for slots, ensuring efficient knowledge representation.

Relationships and associations between frames can be captured, enabling complex dependencies to be modeled. Having this structured approach to organizing knowledge makes information retrieval much more efficient.

Semantic networks

Semantic networks are a graphical knowledge representation technique in AI that organizes knowledge using nodes and arcs. In these networks, nodes represent objects and concepts, while arcs depict relationships. This graphical representation makes it intuitive and easy to understand the knowledge encoded within the network. 

One of the best things about semantic networks is their ability to capture complex relationships. Nodes can have various connections without other nodes, such as custom-defined connections. This flexibility allows for representing diverse types of relationships, including spatial, causal, and temporal associations. 

They also support the concept of inheritance, where nodes can inherit properties and relationships from ones on a higher level. This structure means that the creation of taxonomies can be created and captures the generalization-specialization relationships between concepts.

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AI knowledge representation requirements

Representational adequacy

This refers to the ability of an AI system to accurately capture and represent real-world knowledge or information in a meaningful way. In other words, it involves creating a representation that accurately portrays the facts, concepts, and relationships of the domain being modeled. It’s like building a mental map that helps the AI navigate through the vast landscape of information.

Achieving representational adequacy is essential because AI systems heavily rely on the knowledge they possess to perform various tasks, such as reasoning, problem-solving, and decision-making. 

Inferential adequacy

Inferential adequacy refers to the ability of the knowledge representation system to support reasoning and inference. 

The system should enable the derivation of new knowledge or conclusions based on the available knowledge and rules. It should allow for logical deduction, induction, and probabilistic reasoning to draw inferences and make intelligent decisions.

Inferential efficiency

Inferential efficiency pertains to the efficiency and effectiveness of the reasoning or inference process within the knowledge representation system. 

Inferential efficiency also plays a big part in real-time applications where timely decision-making is crucial. By minimizing computational overhead and optimizing inference procedures, AI systems can provide faster responses, enabling them to be more efficient and adaptive.

Acquisitional efficiency

Acquisition efficiency relates to the ease and efficiency of acquiring knowledge and adding it to the representation system. It involves learning, updating, or adapting knowledge representation with minimal effort and time. 

The system should support efficient mechanisms for acquiring knowledge from various sources, such as experts, databases, or automated learning components.

The cycle of AI knowledge representation

This cycle is the iterative process of creating, refining, and using knowledge representations in AI systems. There are several stages in this cycle: 

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Perception

This stage involves the gathering of information from the environment. It can involve sensors, cameras, or other data sources that capture input from the real world. 

Perception allows the AI system to gather raw data, which serves as the basis for subsequent stages. This can come in the following forms:

  • Images. 
  • Text. 
  • Sensor readings.

Learning

In the learning stage, the AI system processes the acquired data from the previous stage to extract patterns, connections, and insights. Machine learning techniques, such as supervised learning and deep learning algorithms, are typically used to analyze and extract knowledge from the data. 

This stage in the cycle gives the system underlying structures to discover as well as degenerate models or representations of the knowledge.

Knowledge representation and reasoning (KRR)

Once the knowledge has been acquired and learned, it needs to be represented in a structured format that the AI system can manipulate and process. 

It involves selecting an appropriate representation scheme, such as:

  • Logical rules ontologies. 
  • Semantic networks
  • Knowledge graphs. 

The knowledge is organized and encoded using the chosen representation, enabling the system to reason, infer, and make decisions based on the available knowledge. 

Planning

In the planning stage, the AI system uses its knowledge representation to generate plans or sequences of actions that achieve specific goals. 

Planning involves using reasoning capabilities to evaluate different actions, anticipate outcomes, and select the best course of action. The system may use search, optimization, or constraint satisfaction algorithms to generate efficient and effective plans.

Execution

The final stage is the execution stage which involves carrying out the planned actions based on the generated plan. The AI system interacts with the environment, performs tasks, and monitors the outcomes. 

During execution, the system may receive feedback or additional information, which can be used to do the following:

  • Update the knowledge representation.
  • Refine future plans.
  • Adapt its behavior based on the observed results. 

How AI knowledge representation is stored

By matching storage methods to particular domains for AI knowledge representation, systems can better capture and organize knowledge for efficient problem-solving.

AI knowledge representationStorage method
Simple relational knowledgeThis is the representation of knowledge using a basic relational model which organizes things in a structured manner with tables, rows, and columns. 

Facts or propositions are usually stored as statements; each table represents a specific concept. Within these tables are the tows that correspond to individual instances and examples, and the columns represent different attributes. 

While this approach is straightforward, it can have limitations when it comes to capturing complex relationships and uncertainty.
Inheritable knowledgeThis method is a fundamental principle in knowledge representation where instances or objects inherit properties, attributes, and behaviors from their corresponding classes. Classes represent categories of types of objects, while instances represent specific examples of those categories. 

The inheritable knowledge approach allows for the propagation of knowledge from more general classes to specific instances. When an instance is associated with a class, it inherits the defined characteristics. 

This means knowledge can be reused and shared across related objects, reducing redundancy and promoting efficiency in knowledge representation. 
Inferential knowledgeThis refers to the knowledge that is derived through logical reasoning or deduction based on existing information. It plays a crucial role in AI knowledge representation and is stored using various mechanisms. 

Inferential knowledge is represented using rules and formal logic. These rules capture dependencies, relationships, or patterns found in the data. By applying this rule-based system to the knowledge available, new conclusions can be made. 
Procedural knowledgeProcedural knowledge in AI representation refers to the knowledge about processes, procedures, or algorithms required to perform specific tasks. 
It uses languages such as LISP and Prolog and mainly focuses on capturing the steps and instructions necessary to solve problems, make decisions, and execute actions within the system.

It’s worth noting that procedural knowledge is often combined with other types of representation, such as declarative knowledge or statistical models, to enhance the system’s overall capabilities.

To round up

Good knowledge representation systems provide a special set of rules and frameworks that help AI navigate the complex data they encounter and extract meaningful insights, from identifying images to solving complex problems. 

It makes AI advanced decision-makers and can even mimic human intelligence if the right representation methods are used. So, the next time you witness an AI system perform its magic, remember all of the steps it took to get there. 

Were you aware of the special ways in which AI processes data? If you want to learn more about AI, head over to Top Apps.