Abductive Action Inference

Abductive reasoning aims to make the most likely inference for a given set of incomplete observations. In this work, we propose a new task called abductive action inference, in which given a situation, the model answers the question `what actions were executed by the human in order to arrive in the current state?'. Given a state, we investigate three abductive inference problems: action set prediction, action sequence prediction, and abductive action verification. We benchmark several SOTA models such as Transformers, Graph neural networks, CLIP, BLIP, end-to-end trained Slow-Fast, and Resnet50-3D models. Our newly proposed object-relational BiGED model outperforms all other methods on this challenging task on the Action Genome dataset. Codes will be made available.Comment: 16 pages, 9 figure

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

We propose an efficient family of algorithms to learn the parameters of a Bayesian network from incomplete data. In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network. Our approach provides consistent parameter estimates for missing data problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach is orders of magnitude faster than EM (as our approach requires no inference). Given sufficient data, we learn parameters that can be orders of magnitude more accurate

Solving DCOPs with Distributed Large Neighborhood Search

The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances