Knowledge-Based Agent in AI

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A knowledge-based agent in artificial intelligence leverages internal knowledge to make decisions and perform tasks. It employs a knowledge base for reasoning and inference, enabling it to adapt and make informed choices. Knowledge-based agents are built using declarative and procedural approaches, combining explicit knowledge representation with encoded behaviors for effective decision-making and action execution.

What is Knowledge-Based Agent in Artificial intelligence?

A knowledge-based agent in artificial intelligence is an intelligent agent that utilizes knowledge to make decisions and perform tasks. Unlike simple reactive agents that operate based solely on their current perceptual input, knowledge-based agents have an internal knowledge base that they use to reason, plan, and make informed choices. The knowledge base of a knowledge-based agent typically consists of a representation of the world, including facts, rules, and heuristics. This knowledge can be acquired through various means such as manual encoding by human experts, learning from data, or a combination of both.

Knowledge-based agents often employ techniques from symbolic AI, such as logic, knowledge representation languages (e.g., predicate logic, frames, semantic networks), and inference mechanisms (e.g., deduction, abduction, induction). These agents are capable of reasoning about their environment, formulating plans, and adapting their behavior based on their goals and the information available to them. Examples of knowledge-based agents include expert systems, which emulate the decision-making ability of human experts in specific domains, and intelligent assistants, which utilize knowledge to provide personalized recommendations and assistance to users.

Knowledge-based agents are composed of two main parts:

  • Knowledge Base: The knowledge base of a knowledge-based agent is where all relevant information about the domain of interest is stored. It contains facts, rules, heuristics, and other representations of knowledge that the agent can use to reason and make decisions. This knowledge is typically structured in a way that the agent can efficiently access and manipulate it. The knowledge base may be built manually by domain experts or acquired through automated processes such as machine learning. Examples of knowledge stored in the knowledge base could include facts about the environment, rules governing actions, and strategies for achieving goals.
  • Inference System: The inference system is responsible for reasoning and making deductions based on the knowledge stored in the knowledge base. It processes the available information to derive new conclusions, make predictions, or solve problems. The inference system employs various techniques from the field of artificial intelligence, such as logic, probabilistic reasoning, and pattern recognition, to infer new knowledge from the existing knowledge base. It may use different methods of inference, such as deduction, abduction, or induction, depending on the nature of the problem and the available information. The goal of the inference system is to enable the knowledge-based agent to make intelligent decisions and take appropriate actions based on its understanding of the domain.

A knowledge-based agent must able to do the following:

  • State Representation: A knowledge-based agent should possess the capability to represent various states, actions, and other relevant entities within its environment.
  • Dynamic Perception: It should be equipped to seamlessly integrate new percepts into its existing knowledge base, allowing it to adapt and respond effectively to changes in its environment.
  • World Model Updates: The agent must have the ability to update its internal representation of the world in response to new information or changes in its environment, ensuring its understanding remains accurate and up-to-date.
  • Inference: Utilizing its knowledge base, the agent should be capable of deducing insights and drawing conclusions about the internal representation of the world, aiding in its decision-making processes.
  • Action Deduction: Lastly, the agent should be able to deduce appropriate actions based on its understanding of the environment, effectively translating its knowledge into actionable strategies to achieve its goals.

The architecture of knowledge-based agent:

The diagram above illustrates the architecture of a knowledge-based agent (KBA). In this architecture, the KBA interacts with its environment through perception, allowing it to gather input and information. This input is then processed by the inference engine, a crucial component of the agent, which communicates with the Knowledge Base (KB). The KB serves as the repository of the agent's knowledge, containing facts, rules, and heuristics pertinent to its domain.

The architecture of knowledge-based agent

The Inference Engine leverages the knowledge stored in the KB to make decisions and draw conclusions based on the received input. It utilizes various reasoning mechanisms such as deduction, abduction, and induction to infer new insights and formulate appropriate responses.

Moreover, the learning element of the KBA plays a vital role in continually updating and refining the KB. Through learning algorithms and processes, the KBA assimilates new knowledge acquired from its interactions with the environment, enabling it to adapt and improve its performance over time.

Overall, this architecture embodies the capabilities of a knowledge-based agent, facilitating intelligent decision-making and adaptive behavior through the seamless integration of perception, inference, and learning mechanisms.

Why do we use a knowledge base?

  • Information Storage: A knowledge base allows us to store vast amounts of information in an organized and structured manner. This includes facts, rules, heuristics, and other domain-specific knowledge that is relevant to the system or agent.
  • Knowledge Sharing: By centralizing knowledge in a knowledge base, it becomes accessible to all components of a system or agent, facilitating collaboration and information sharing. This ensures consistency and coherence in decision-making processes.
  • Reasoning and Inference: The knowledge stored in a knowledge base forms the basis for reasoning and inference. It enables systems or agents to draw conclusions, make predictions, and derive new knowledge based on existing information.
  • Decision Making: Knowledge bases provide the necessary information for making informed decisions. They enable systems or agents to evaluate options, assess risks, and determine the most appropriate course of action based on their goals and objectives.
  • Adaptation and Learning: Knowledge bases can be updated and expanded over time, allowing systems or agents to adapt to changing environments and learn from new experiences. This enables continuous improvement and optimization of performance.
  • Expertise Capture: In expert systems, knowledge bases are used to capture and encode the expertise of human experts in specific domains. This allows the system to emulate the decision-making capabilities of experts and provide valuable insights and recommendations.

Inference system:

Inference, within the context of knowledge-based systems, involves deriving new propositions or sentences from existing ones. An inference system serves as the mechanism for adding new information to the knowledge base (KB), where each sentence represents a proposition about the world. By applying logical rules to the KB, the inference system deduces new information, expanding the agent's understanding of its environment.

The primary function of the inference system is to generate new facts that allow the agent to update the KB dynamically. This process occurs through two main approaches:

  1. Forward Chaining: In forward chaining, the inference system starts with known facts and applies inference rules to deduce new conclusions. It iteratively examines the KB, identifying applicable rules and generating new facts until no further deductions can be made.
  2. Backward Chaining: Conversely, backward chaining starts with a goal or desired outcome and works backward to determine the conditions under which the goal can be achieved. It recursively applies inference rules to the KB, tracing back from the goal to the initial conditions, thereby identifying the actions or steps necessary to fulfill the goal.

Both forward chaining and backward chaining are essential techniques in the inference process, allowing the inference system to systematically derive new information and update the KB. By employing these rules, the inference system enhances the agent's reasoning capabilities and enables it to make informed decisions based on its evolving understanding of the world.

Operations Performed by a Knowledge-Based Agent (KBA):

There are three primary operations that a KBA executes to demonstrate intelligent behavior:

  1. TELL: This operation involves informing the knowledge base about the observations or perceptions gathered from the environment.
  2. ASK: In this operation, the KBA queries the knowledge base to obtain recommendations or insights on the appropriate action to take.
  3. Perform: Once the decision is made based on the information provided by the knowledge base, the KBA executes the selected action to interact with the environment.

These operations collectively enable the KBA to perceive, reason, and act intelligently, facilitating its ability to make informed decisions and adapt to varying situations.

A generic knowledge-based agent:

Below is the outline structure of a generic knowledge-based agent program:


    function KB_AGENT(percept):
        persistent: KB, a knowledge base
        persistent: t, a counter, initially 0, indicating time
        
        TELL(KB, MAKE_PERCEPT_SENTENCE(percept, t))
        action = ASK(KB, MAKE_ACTION_QUERY(t))
        TELL(KB, MAKE_ACTION_SENTENCE(action, t))
        
        t = t + 1
        return action

A generic knowledge-based agent operates by taking a percept as input and producing an action as output. The agent maintains a knowledge base (KB), initially equipped with background knowledge about the real world. Additionally, it employs a counter to track the passage of time, initialized to zero.

Upon each invocation, the agent performs three key operations:

  1. TELL: The agent informs the KB about the perceived information.
  2. ASK: It queries the KB to determine the appropriate action to take.
  3. TELL (Action Chosen): The agent communicates to the KB the action it has decided to execute.

The MAKE-PERCEPT-SENTENCE function generates a sentence indicating that the agent perceived the given percept at the specified time.

The MAKE-ACTION-QUERY function generates a sentence to inquire about the action to be taken at the current time.

The MAKE-ACTION-SENTENCE function generates a sentence asserting that the chosen action has been executed.

Various levels of knowledge-based agent:

A knowledge-based agent can be conceptualized and analyzed at various levels, as outlined below:

  • Knowledge Level: The knowledge level is the foundational level of a knowledge-based agent. At this level, we specify what the agent knows and what its goals are, thereby determining its behavior. For instance, if an automated taxi agent needs to travel from station A to station B and already knows the route, this falls within the realm of the knowledge level.
  • Logical Level: At the logical level, we delve into how knowledge is represented and stored. Here, sentences are encoded into different logical forms. This level involves the encoding of knowledge into logical sentences. For example, at the logical level, we can expect the automated taxi agent to use logical reasoning to plan and execute its route to reach destination B.
  • Implementation Level: The implementation level represents the physical manifestation of the logic and knowledge. At this level, the agent translates its knowledge and logical reasoning into actions. This is where the automated taxi agent actually implements its knowledge and logic to navigate and reach its destination effectively.

Approaches to designing a knowledge-based agent:

There are primarily two approaches for constructing a knowledge-based agent:

  • Declarative Approach: This approach involves initializing a knowledge-based agent with an empty knowledge base and providing it with all the sentences or information it needs to start with. This process is known as the declarative approach, where the agent is informed about its initial state and knowledge explicitly.
  • Procedural Approach: In the procedural approach, the desired behavior of the knowledge-based agent is directly encoded into a program code. Rather than explicitly stating the initial knowledge, this approach involves writing a program that embodies the desired behavior or actions of the agent. The agent's behavior is determined by the logic and instructions contained within the program.

In practice, successful agents are often developed by combining both declarative and procedural approaches. Declarative knowledge, which specifies facts and rules, can be compiled into more efficient procedural code, resulting in a more effective agent.