Introduction Prerequisites This howto is written assuming that you have basic understanding of Java and that you are capable of downloading Maven and get it on your command line. You may also want to choose an IDE (Eclipse or Intellij are both good choices). For more information about what JADE is, visit Their main website. […]

# Category: Algorithmic

The final stop on this heuristics tour, and the last stop for our overview of Cooperative Decision Making is Joint Equilibrium Search. This technique starts with some pre-set horizon T policies for each agent, and then cycles through each agent so that it may tweak its behaviors to maximize the response with all other policies […]

Memory bounded dynamic programming is another technique offered in Cooperative Decision Making. This is the first sub-optimal heuristic that is brought up. It takes the same techniques as seen before with an exhaustive backup, but at each stage, only a specific number of trees remain at the end of these operations. Due to this, the […]

## Tour de Heuristics: Policy Iteration

Policy Iteration is the most available option for dealing with infinite horizon DEC-POMDP’s. In this space, it is sub-optimal. It can be, however, epsilon-optimal. Epsilon optimality means that based on the starting point and a decay factor, we can plan a controller out for enough steps that the expected discounted reward for any more steps […]

## Tour de Heuristics: MAA*

Multiagent A* is a heuristic that takes the commonly used A* algorithm and applies it to Dec-POMDP’s. Let’s investigate how it works. The Algorithm def estimated_state_value(belief, action): """ The cornerstone of the A* algorithm is to have an optimistic estimator. Because an MDP assumes more information, it will always have at least as much value […]

## Multi-Agent Systems: Finite Horizons

In our previous post, we covered the basics of what a Dec-POMDP is. Let’s actually look at what a policy is and how we can generate one. The Example In this example, there are two robots that are trying to meet anywhere on the map. They want to do this optimally. Unfortunately, they don’t have […]

This is the first post in a study on all things using Decentralized Partially Observable Markov Decision Processes (Dec-POMDP) with my professor, Prithviraj Dasgupta, who runs the CMantic Lab at the University of Nebraska, Omaha. I intend to write the summaries of what I find as blog posts, so be prepared to go on a […]

I have hear the words “Markov” more than a few times, but it is only recently that I can appreciate exactly what this simplification buys me. Markov was a guy who liked to model things by using only the current state. This simplification is often very appropriate and often offers a relatively accurate approximation depending […]