What are Knowledge-Based Agents in Artificial Intelligence? Find Out!

- What are Knowledge-Based Agents in Artificial Intelligence?
- What is the Importance of a Knowledge Base?
- The Core Components of Knowledge-Based Agents in AI
- How Do Knowledge-Based Agents in Artificial Intelligence Work?
- Levels of Knowledge-Based Agents
- Approaches to Building Knowledge-Based Agents
- Advantages and Disadvantages of Knowledge-Based Agents in Artificial Intelligence
There is hardly any aspect of daily life that is not touched by AI today. From virtual assistants such as Alexa and Siri simplifying daily tasks to businesses leveraging AI technologies for operational efficiency, the world is witnessing a technological transformation. India, with its 2.47% AI adoption maturity rate, has emerged as a prominent contender in this revolution (1). While terms like generative AI and neural networks have entered common parlance, certain concepts remain elusive for lay enthusiasts and AI newcomers. One such concept is knowledge-based agents in artificial intelligence. So, what exactly is a knowledge-based agent in artificial intelligence, and where does it stand in relation to the vast paradigm of AI?
What are Knowledge-Based Agents in Artificial Intelligence?

To understand a knowledge-based agent, we first need to grasp the concept of agents in AI. So, what is an agent in the context of AI? Simply put, an agent is any computational system that observes and interprets its surroundings through sensory inputs, such as cameras, microphones, or data feeds. It then performs actions using actuators, like robotic arms, speakers, or digital interfaces, to fulfill predefined objectives or solve specific tasks. For example, a robot vacuum cleaner perceives obstacles using sensors and adjusts its movement to clean effectively without collisions. Similarly, an autonomous vehicle collects environmental data using cameras and other sensors, and then uses actuators to provide a smooth driving experience.
AI agents can be categorized into different types, such as:
- Simple reflex agents rely on condition-action rules to respond to stimuli without taking historical data into account
- Model-based agents use internal models of their environment to make informed decisions
- Goal-based agents consider a possible range of actions and attempt to achieve specific goals
- Utility-based agents aim to maximize utility or satisfaction by weighing different outcomes
- Learning-based agents have the unique ability to learn from experience and adapt to new scenarios by improving their knowledge and refining their decision-making algorithms over time
Now, where do knowledge-based agents in artificial intelligence fit within this spectrum? Unlike the categories above, a knowledge-based agent is not a subset of any single type but a distinct paradigm. Its defining feature is its dependence on a structured knowledge base. This repository of information allows the agent to store facts, rules, and relationships about its environment in a systematic way. The agent uses this knowledge, combined with an inference mechanism, to reason, make decisions, and solve complex problems.
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What is the Importance of a Knowledge Base?
A knowledge base is indispensable for providing contextual insights and a profound understanding of the environment, enabling the system to make informed decisions. In essence, it is a structured repository of information that an agent can draw upon to make decisions. Now, the importance of a knowledge base lies in its ability to provide context and depth. For example, in a medical diagnostic system, the knowledge base might contain information about symptoms, diseases, and treatment protocols. This consequently enables the agent to infer a likely diagnosis based on observed symptoms. Without this repository, the agent’s functionality would be rudimentary at best. Thus, in essence, the knowledge base transforms an AI agent from a reactive system to a proactive one, capable of nuanced decision-making.
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The Core Components of Knowledge-Based Agents in AI
Knowledge-based agents in artificial intelligence are built on two fundamental components: the knowledge base and the inference system.
1. Knowledge Base (KB)
The knowledge base is a structured database containing facts and rules about a domain. In essence, it stores information in a format that is both retrievable and manipulable by the inference system. The KB might use formal languages like predicate logic to represent knowledge systematically. For instance, “every living being will die one day” could be encoded as a logical statement in the KB, allowing the agent to derive conclusions about specific humans.
2. Inference System
The inference system processes the information stored in the KB to draw logical conclusions. It uses methods like forward chaining and backward chaining to reason about data and solve problems. For example, given a rule that states, “If it rains, the ground gets wet”, the inference system can deduce that the ground will be wet if it detects rain.
Together, the KB and inference system enables knowledge-based agents in artificial intelligence to simulate human-like reasoning.
How Do Knowledge-Based Agents in Artificial Intelligence Work?
The architecture of knowledge-based agents in artificial intelligence follows a step-by-step process:
- Perception: The agent perceives its environment through sensors, collecting raw data about its surroundings
- Knowledge acquisition: The agent updates its knowledge base with new information, integrating it with existing knowledge to create a coherent understanding of the environment
- Inference: Using its inference system, the agent applies logical reasoning to the knowledge base to derive actionable insights or predictions
- Decision-making: The agent evaluates possible courses of action and selects the one that aligns with its goals or maximizes utility
- Execution: Finally, the agent executes the chosen action using its actuators, thereby interacting with its environment
This iterative cycle empowers the agent to evolve its behavior, refine its understanding, and enhance its performance with each interaction.
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Levels of Knowledge-Based Agents

Knowledge-based agents in artificial intelligence operate at three distinct levels, and each of them contributes to the overall functionality of a system:
- Knowledge level: At this level, the agent focuses on understanding and organizing knowledge. It ensures that the knowledge base is accurate, consistent, and relevant to the agent’s tasks
- Logical level: Here, the agent represents knowledge using formal logic. It employs logical frameworks to infer new facts, identify contradictions, and validate conclusions
- Implementation level: This level deals with the practical aspects of building the agent, including algorithms, data structures, and programming paradigms
Approaches to Building Knowledge-Based Agents
Two primary approaches exist for constructing knowledge-based agents in artificial intelligence: declarative and procedural. The choice between these approaches depends on the application’s complexity and requirements.
1. Declarative Approach
In the declarative approach, knowledge is represented explicitly as facts and rules within the KB. In sum, this approach allows for greater flexibility, as the agent can reason about knowledge without predefined algorithms.
2. Procedural Approach
The procedural approach embeds knowledge within specific algorithms. While this method offers faster processing, it sacrifices the flexibility of reasoning across diverse scenarios.
Advantages and Disadvantages of Knowledge-Based Agents in Artificial Intelligence
1. Advantages
- Versatility: These agents can tackle complex, domain-specific problems
- Scalability: The KB can grow over time, allowing the agent to learn and adapt
- Reasoning capability: These agents excel at logical inference and decision-making
2. Disadvantages
- Computational overhead: Managing and querying large KBs can be resource-intensive
- Dependency on data quality: The agent’s performance depends on the accuracy of its knowledge base
- Complexity: Building and maintaining a knowledge-based system requires significant expertise
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Knowledge-based agents in artificial intelligence stand out as critical innovations that leverage a structured knowledge base and inference mechanisms to perform logical reasoning and solve intricate problems. By understanding and implementing such advanced AI tools, we unlock the potential for smarter systems across industries. This becomes especially important in the context of the fact that India has emerged as a global leader in AI adoption, boasting a 2.47% adoption rate and driving innovation at an unprecedented pace. As a result, this rapid growth is translating into tangible opportunities, with AI specialist roles projected to surge by 39% by this year. (2, 3).
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