Addressing multiagent decision problems in AI, especially those involving collaborative or competitive agents acting concurrently in a partially observable and stochastic environment, remains a formidable challenge. While Interactive Dynamic Influence Diagrams~(I-DIDs) have offered a promising decision framework for such problems, they encounter limitations when the subject agent encounters unknown behaviors exhibited by other agents that are not explicitly modeled within the I-DID. This can lead to sub-optimal responses from the subject agent. In this paper, we propose a novel data-driven approach that utilizes an encoder-decoder architecture, particularly a variational autoencoder, to enhance I-DID solutions. By integrating a perplexity-based tree loss function into the optimization algorithm of the variational autoencoder, coupled with the advantages of Zig-Zag One-Hot encoding and decoding, we generate potential behaviors of other agents within the I-DID that are more likely to contain their true behaviors, even from limited interactions. This new approach enables the subject agent to respond more appropriately to unknown behaviors, thus improving its decision quality. We empirically demonstrate the effectiveness of the proposed approach in two well-established problem domains, highlighting its potential for handling multi-agent decision problems with unknown behaviors. This work is the first time of using neural networks based approaches to deal with the I-DID challenge in agent planning and learning problems.

Interactive dynamic influence diagrams (I-DIDs) are graphical models for  sequential  decision making  in  partially observable  settings shared  by other  agents.  Algorithms  for solving  I-DIDs  face the challenge  of an  exponentially  growing space  of candidate  models ascribed to  other agents, over time. Previous  approach for exactly   solving I-DIDs groups together  models having similar solutions into behaviorally  equivalent  classes  and  updates these  classes.   We present a  new method that, in addition  to aggregating behaviorally equivalent  models, further groups  models that  prescribe identical actions at a single time step. We show how to update these augmented classes  and prove  that  our  method is  exact.   The new  approach   enables us to bound the aggregated model space by the cardinality of   other  agents'  actions. We  evaluate  its  performance and  provide   empirical results in support.