Searching with Partial Observations

 Searching with partial observations is a subfield of AI concerned with developing techniques for decision making under uncertainty, where an agent does not have full access to the current state of the environment. In such cases, the agent must make decisions based on a partially observable state of the environment.

One of the key techniques used in searching with partial observations is the use of belief states, which are probability distributions over the possible states of the environment. Belief states are updated using Bayesian inference as new observations are made.

One common approach to searching with partial observations is to use the partially observable Markov decision process (POMDP) framework. In POMDPs, the state of the environment is not fully observable, and the agent must maintain a belief state to represent the possible states of the environment. The agent uses a policy to determine its actions based on its current belief state and the observations it has made.

Solving POMDPs is computationally complex due to the large number of possible belief states. Several approximate techniques have been developed to address this issue, including Monte Carlo methods, particle filtering, and value function approximation.

Another approach to searching with partial observations is to use the hidden Markov model (HMM) framework. In HMMs, the state of the environment is fully observable, but the agent only has access to a sequence of observations. The agent must use this sequence of observations to infer the most likely sequence of states that led to those observations. This is commonly known as the decoding problem, and can be solved using techniques such as the Viterbi algorithm.

Searching with partial observations has applications in a variety of domains, including robotics, natural language processing, and finance. For example, in robotics, an autonomous vehicle may need to navigate through a partially observable environment, where it only has access to a limited set of sensor data. In finance, traders may need to make decisions based on limited information about the market.