no code implementations • 27 Jan 2023 • Niko A. Grupen, Michael Hanlon, Alexis Hao, Daniel D. Lee, Bart Selman
Large-scale AI systems that combine search and learning have reached super-human levels of performance in game-playing, but have also been shown to fail in surprising ways.
no code implementations • 20 Sep 2022 • Dieqiao Feng, Carla P. Gomes, Bart Selman
We propose a domain-independent method that augments graph search with graph value iteration to solve hard planning instances that are out of reach for domain-specialized solvers.
no code implementations • 28 Jun 2022 • Dieqiao Feng, Carla Gomes, Bart Selman
To better understand why these approaches work, we studied the interplay of the policy and value networks of DNN-based best-first search on Sokoban and show the surprising effectiveness of the policy network, further enhanced by the value network, as a guiding heuristic for the search.
no code implementations • NeurIPS 2020 • Dieqiao Feng, Carla P. Gomes, Bart Selman
In particular, as the size of the instance pool increases, the ``hardness gap'' decreases, which facilitates a smoother automated curriculum based learning process.
no code implementations • 29 Sep 2021 • Dieqiao Feng, Carla P Gomes, Bart Selman
To better understanding why these approaches work we study the interplay of the policy and value networks in A\textsc{*}-based deep RL and show the surprising effectiveness of the policy network, further enhanced by the value network, as a guiding heuristic for A\textsc{*}.
no code implementations • 21 Aug 2021 • Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra, Lan Zhou, Bart Selman, R. Bruce van Dover, John M. Gregoire, Carla P. Gomes
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
no code implementations • 31 Jul 2021 • Xiaodong Xin, Kun He, Jialu Bao, Bart Selman, John E. Hopcroft
Our previous work proposes a general structure amplification technique called HICODE that uncovers many layers of functional hidden structure in complex networks.
no code implementations • 21 Jun 2021 • Niko A. Grupen, Daniel D. Lee, Bart Selman
We show that pursuers trained with our strategy exchange more than twice as much information (in bits) than baseline methods, indicating that our method has learned, and relies heavily on, the exchange of implicit signals.
no code implementations • 10 Jun 2021 • Niko A. Grupen, Bart Selman, Daniel D. Lee
We study fairness through the lens of cooperative multi-agent learning.
no code implementations • 30 Nov 2020 • Niko A. Grupen, Daniel D. Lee, Bart Selman
In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 4 Jun 2020 • Dieqiao Feng, Carla P. Gomes, Bart Selman
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems.
no code implementations • 7 Aug 2019 • Yolanda Gil, Bart Selman
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society.
no code implementations • NeurIPS 2018 • Johan Bjorck, Carla Gomes, Bart Selman, Kilian Q. Weinberger
Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks.
no code implementations • 23 May 2017 • Xiaojian Wu, Yexiang Xue, Bart Selman, Carla P. Gomes
In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated.
no code implementations • NeurIPS 2016 • Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them.
no code implementations • 11 Mar 2016 • Chenxia Wu, Jiemi Zhang, Ozan Sener, Bart Selman, Silvio Savarese, Ashutosh Saxena
For evaluation, we contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacting with different objects.
no code implementations • 14 Dec 2015 • Chenxia Wu, Jiemi Zhang, Bart Selman, Silvio Savarese, Ashutosh Saxena
We show that our approach not only improves the unsupervised action segmentation and action cluster assignment performance, but also effectively detects the forgotten actions on a challenging human activity RGB-D video dataset.
no code implementations • 17 Aug 2015 • Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman
The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models.
no code implementations • 27 Nov 2014 • Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla Gomes, Bart Selman, Robert B. van Dover
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining.
no code implementations • NeurIPS 2013 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model.
no code implementations • 26 Sep 2013 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
Many probabilistic inference tasks involve summations over exponentially large sets.
no code implementations • 24 Jun 2013 • Jaeyong Sung, Bart Selman, Ashutosh Saxena
Many tasks in human environments require performing a sequence of navigation and manipulation steps involving objects.
no code implementations • NeurIPS 2012 • Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes
Given a probabilistic graphical model, its density of states is a function that, for any likelihood value, gives the number of configurations with that probability.
1 code implementation • 2012 IEEE International Conference on Robotics and Automation 2012 • Jaeyong Sung, Colin Ponce, Bart Selman, Ashutosh Saxena
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics.
no code implementations • NeurIPS 2011 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function.