Neural Reasoning About Agents' Goals, Preferences, and Actions
This addresses the challenge of generalizing agent reasoning to new situations in AI, though it appears incremental as it builds on existing neural methods with specific enhancements.
The paper tackled the problem of intuitive psychological reasoning about agents' goals, preferences, and actions by proposing the Intuitive Reasoning Network (IRENE), which achieved new state-of-the-art performance on three out of five tasks in the Baby Intuitions Benchmark with up to 48.9% improvement.
We propose the Intuitive Reasoning Network (IRENE) - a novel neural model for intuitive psychological reasoning about agents' goals, preferences, and actions that can generalise previous experiences to new situations. IRENE combines a graph neural network for learning agent and world state representations with a transformer to encode the task context. When evaluated on the challenging Baby Intuitions Benchmark, IRENE achieves new state-of-the-art performance on three out of its five tasks - with up to 48.9% improvement. In contrast to existing methods, IRENE is able to bind preferences to specific agents, to better distinguish between rational and irrational agents, and to better understand the role of blocking obstacles. We also investigate, for the first time, the influence of the training tasks on test performance. Our analyses demonstrate the effectiveness of IRENE in combining prior knowledge gained during training for unseen evaluation tasks.