Search Results for author:
Found 25 papers, 2 papers with code
Policy-Value Alignment and Robustness in Search-based Multi-Agent Learning
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.
Graph Value Iteration
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.
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning
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.
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
In particular, as the size of the instance pool increases, the ``hardness gap'' decreases, which facilitates a smoother automated curriculum based learning process.
The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning
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{*}.
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
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.
Structure Amplification on Multi-layer Stochastic Block Models
Our previous work proposes a general structure amplification technique called HICODE that uncovers many layers of functional hidden structure in complex networks.
Multi-Agent Curricula and Emergent Implicit Signaling
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.
Cooperative Multi-Agent Fairness and Equivariant Policies
We study fairness through the lens of cooperative multi-agent learning.
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems
In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems.
A 20-Year Community Roadmap for Artificial Intelligence Research in the US
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society.
Understanding Batch Normalization
Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks.
XOR-Sampling for Network Design with Correlated Stochastic Events
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.
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
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.
Watch-n-Patch: Unsupervised Learning of Actions and Relations
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.
Watch-Bot: Unsupervised Learning for Reminding Humans of Forgotten Actions
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.
Variable Elimination in the Fourier Domain
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.
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining.
Embed and Project: Discrete Sampling with Universal Hashing
We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model.
Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Many probabilistic inference tasks involve summations over exponentially large sets.
Synthesizing Manipulation Sequences for Under-Specified Tasks using Unrolled Markov Random Fields
Many tasks in human environments require performing a sequence of navigation and manipulation steps involving objects.
Density Propagation and Improved Bounds on the Partition Function
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.
Unstructured Human Activity Detection from RGBD Images
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics.
Accelerated Adaptive Markov Chain for Partition Function Computation
We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function.
