109 citations · 410 across the 44 of their papers we have counts for
13 papers · 1 filter
Have I Scene This Before? Spatially Grounded Conversational Memory for Complex Queries in Egocentric Assistants
Jiazhou Liang, Liam Gallagher, Kiko Chen +4
Egocentric assistants must connect what users say with what they see across long interaction histories. We formalize this challenge as Spatially grounded Conversational Reasoning (…
LogicRec: Recommendation with Users' Logical Requirements
Zhenwei Tang, Griffin Floto, Armin Toroghi +3
Users may demand recommendations with highly personalized requirements involving logical operations, e.g., the intersection of two requirements, where such requirements naturally f…
Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus
Yudong Xu, Elias B. Khalil, Scott Sanner
The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms. The ARC's focus on broad generalization and few-shot…
pyRDDLGym: From RDDL to Gym Environments
Ayal Taitler, Michael Gimelfarb, Jihwan Jeong +4
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is…
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs
Siow Meng Low, Akshat Kumar, Scott Sanner
Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and…
Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021
Buser Say, Scott Sanner, Jo Devriendt +2
This document provides a brief introduction to learned automated planning problem where the state transition function is in the form of a binarized neural network (BNN), presents a…
Reward Potentials for Planning with Learned Neural Network Transition Models
Buser Say, Scott Sanner, Sylvie Thiébaux
Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for br…
Scalable Planning with Deep Neural Network Learned Transition Models
Ga Wu, Buser Say, Scott Sanner
In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and…
Stochastic Planning and Lifted Inference
Roni Khardon, Scott Sanner
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorit…
Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs
Luis Gustavo Vianna, Scott Sanner, Leliane Nunes de Barros
Recent advances in symbolic dynamic programming (SDP) combined with the extended algebraic decision diagram (XADD) data structure have provided exact solutions for mixed discrete a…
Approximate Linear Programming for First-order MDPs
Scott Sanner, Craig Boutilier
We introduce a new approximate solution technique for first-order Markov decision processes (FOMDPs). Representing the value function linearly w.r.t. a set of first-order basis fun…
Practical Linear Value-approximation Techniques for First-order MDPs
Scott Sanner, Craig Boutilier
Recent work on approximate linear programming (ALP) techniques for first-order Markov Decision Processes (FOMDPs) represents the value function linearly w.r.t. a set of first-order…
Symbolic Dynamic Programming for Discrete and Continuous State MDPs
Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros
Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addres…