With the power of POMDPs (Partially Observable Markov Decision Processes), we can now make conservation decisions with confidence.

To kick things off: what is a POMDP problem for conservation decisions? It’s basically a fancy way to say that we have an environment where we don’t know everything, but we need to make the best decision possible based on our limited knowledge. In this case, the environment is the habitat of endangered species and our goal is to protect them from extinction.

Here are some steps you can follow to create your own POMDP problem for conservation decisions:

1. Define your state space: This is where we list all possible states that the environment could be in. For example, if we’re trying to conserve a population of tigers, our state space might include things like “tiger density high,” “habitat destruction low,” and “poaching incidents rare.”

2. Define your action space: This is where we list all possible actions that can be taken in the environment. For example, if we’re trying to conserve a population of tigers, our action space might include things like “increase patrols,” “plant more trees,” and “educate local communities.”

3. Define your observation function: This is where we list all possible observations that can be made in the environment. For example, if we’re trying to conserve a population of tigers, our observation function might include things like “tiger sightings,” “habitat destruction detected,” and “poaching incidents reported.”

4. Define your transition model: This is where we list all possible transitions between states based on the actions taken in the environment. For example, if we’re trying to conserve a population of tigers, our transition model might include things like “if we increase patrols and plant more trees, then habitat destruction will decrease,” or “if poaching incidents are reported, then tiger density will decrease.”

5. Define your reward function: This is where we list all possible rewards that can be earned in the environment based on our actions. For example, if we’re trying to conserve a population of tigers, our reward function might include things like “if tiger density increases, then we earn 10 points,” or “if habitat destruction decreases, then we earn 5 points.”

6. Define your discount factor: This is where we set the value for how much future rewards are worth compared to current rewards. For example, if we’re trying to conserve a population of tigers and our discount factor is 0.9, then earning 10 points in the future is equivalent to earning 9 points now.

7. Define your policy: This is where we choose which action to take based on our current state and observation. For example, if we’re trying to conserve a population of tigers and our current state is “tiger density high” and our observation is “poaching incidents reported,” then our policy might be to increase patrols and educate local communities.

8. Run your POMDP solver: This is where you let the computer do all the hard work for you! There are many different POMDP solvers available, but some popular ones include Point-Based Value Iteration (PBVI), Monte Carlo Tree Search (MCTS), and Particle Filtering.

And that’s it! With these steps, you can create your own POMDP problem for conservation decisions and let the computer help you make the best decision possible based on our limited knowledge of the environment. Who needs humans when we have computers to save endangered species?