A Particle Filtering Algorithm for Interactive POMDPs
نویسندگان
چکیده
Interactive POMDP (I-POMDP) is a stochastic optimization framework for sequential planning in multiagent settings. It represents a direct generalization of POMDPs to multiagent cases. Expectedly, I-POMDPs also suffer from a high computational complexity, thereby motivating approximation schemes. In this paper, we propose using a particle filtering algorithm for approximating the I-POMDP belief update process. Since the belief update is a key step in solving I-POMDPs, approximating it will reduce the time its takes to compute the solution.
منابع مشابه
A Particle Filtering Based Approach to Approximating Interactive POMDPs
POMDPs provide a principled framework for sequential planning in single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent’s belief about the physical world, about beliefs of the other agent(s), about their beliefs about others’ beliefs, and so on....
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