Fully Observable vs. Partially Observable Environment in AI

In AI, an environment serves as an external stimulus to which the agent perceives and reacts. Through sensors, an agent receives input from the environment, and through actuators, it executes actions. The environment sets the conditions for the agent to achieve its goals.

For instance, in the case of an autonomous vehicle, factors like road conditions, traffic, weather, and speed limits are considered. In essence, the environment presents a problem to which the agent seeks to provide a solution. It determines a condition for an agent to reach its goal.

In short, an environment is a problem to which the agent is a solution.

Task environments in AI can be categorized into several fundamental types, aiding in the design of agents based on specific techniques. One such categorization includes fully observable and partially observable environments.

Environments in which an agent has complete access to all relevant, necessary information through the sensors for decision-making at every point in time are known as fully observable environments. In other words, the agent can directly perceive all relevant information necessary for decision-making without any ambiguity or uncertainty. The completeness of observation ensures that the agent knows every aspect of the environment that is pertinent to selecting an appropriate action. The relevance of information is determined by the performance measure, which specifies the criteria for evaluating the agent’s actions.

In a fully observable environment, the agent does not need to maintain an internal state to keep track of the world because all relevant information is readily available through its sensors. This characteristic simplifies the decision-making process for the agent, as it can base its actions solely on the current state of the environment without needing to consider past states extensively or predict future states. Fully observable environments are convenient for AI systems because they facilitate straightforward strategies and often lead to deterministic behaviour, allowing agents to efficiently navigate and interact with the environment.

Characteristics of Fully Observable Environments

1. Complete Access: Agents have unrestricted access to the entire state of the environment at any given time.
2. No Hidden Information: There are no hidden or unobservable states, allowing agents to perceive all relevant aspects directly.
3. Clarity in Decision-Making: With complete information available, decision-making is straightforward and deterministic.
4. Minimal Memory Requirement: Agents do not need to maintain extensive internal states or histories of past observations.
5. Efficient Strategies: Agents can employ reactive strategies, responding efficiently to immediate stimuli without complex computations or memory management.

Challenges of Fully Observable Environments

Do not work for complex applications because real-life situations do not happen in a controlled way.

Arises a risk of overfitting, where the model learns to perform well on training data but fails to generalize to unseen data.

Note: Playing chess can be partially observable if the opponent is not playing for a win.

Example of Fully observable environments

Fully observable environments are well-suited for AI applications with small and well-defined state spaces. Examples include classic board games like chess and tic-tac-toe. In these environments, algorithms such as depth-first search, breadth-first search, and Q-learning can effectively navigate and exploit available information, enabling straightforward decision-making based on complete information.

Environments in which the agent’s sensors provide access to only partial or incomplete information about the state of the environment at each point in time. are known as partially observable environments. Unlike fully observable environments where the agent has complete access to all relevant aspects of the environment, in a partially observable environment, certain states or factors may be obscured, uncertain, or missing from the sensor data.

The absence of complete information creates difficulties in decision-making because the agent might not fully grasp the present situation or find it challenging to predict future situations with accuracy. Partial observability may result from different causes, such as imprecise sensors, restricted sensor range, or the complexity of the environment.

In a partially observable environment, agents must employ strategies to address partial observability include state estimation, probabilistic reasoning, and memory utilization. Formal frameworks such as Partially Observable Markov Decision Processes (POMDPs) are commonly used to model and solve problems in such environments, enabling agents to develop sophisticated strategies that balance exploration and exploitation.

Characteristics of Partially Observable Environments

1. Incomplete Perception: Agents’ sensors provide access to only partial or incomplete information about the environment’s state at each point in time.
2. Uncertainty: The agent’s understanding of the environment is uncertain due to obscured or missing information, making decision-making challenging.
3. Limited Access: Certain states or factors may be obscured, uncertain, or unavailable from the sensor data, hindering the agent’s ability to perceive the complete environment.
4. Complex Decision-Making: Agents must employ strategies to cope with uncertainty, such as state estimation, probabilistic reasoning, or memory utilization, to make informed decisions.
5. Need for Sophisticated Techniques: Dealing with partial observability requires the use of advanced techniques like Partially Observable Markov Decision Processes (POMDPs) to model and solve problems effectively.

Challenges of Partially Observable Environments

• Agents in partially observable environments may struggle to find optimal solutions due to incomplete information.
• Dealing with partial observability often requires significant memory resources, posing additional challenges.
• Noises in these environments can make it difficult for agents to differentiate between signal and noise.
• Predicting missing states to compensate for partial observability is difficult, requiring the development of complex prediction models.

Example of Partially Observable Environments

Examples of partially observable environments include scenarios where sensors provide limited or obscured information, such as a robot navigating in a cluttered environment with occluded objects or an autonomous vehicle operating in traffic with obscured visibility due to weather conditions.

Fully observable and partially observable environments are two basic paradigms in AI, each bringing its own distinct characteristics and challenges. While completely observable environments are simple and deterministic, partially observable environments require advanced techniques to effectively manage uncertainty and incomplete information.

As AI progresses further, it will be essential to comprehend and tackle the complexities of various environments in order to create intelligent systems that can function efficiently in a wide range of real-life situations. By utilizing knowledge from fully and partially observable environments, AI experts can lead the development of stronger and more flexible AI solutions.

Q. What is AI Environment?

An AI environment is the external stimuli or conditions that an AI agent perceives and responds to. It includes everything that impacts the agent’s decision-making, such as input data, tasks, and constraints.

Q. What is the difference between fully observable or partial observable Environment?

In a completely observable environment, the agent can access all relevant information, but in a partially observable environment, some information may be unclear or unknown, making decision-making more difficult.

Q. Which is better fully observable or partial observable Environment?

The choice between fully observable and partially observable environments is based on the particular task and needs. Completely observable environments are straightforward, while partially observable environments offer complexity and difficulty. Neither one is inherently superior; the most appropriate choice depends on the specific context and objectives of the AI program.

Q. Give an example of each fully observable and partial observable Environment?

• Fully Observable Environment: Chess, where players can see the entire board.
• Partially Observable Environment: Autonomous driving, where sensors provide incomplete information about the surroundings.