23 citations · 171 across the 35 of their papers we have counts for
10 papers · 1 filter
Adaptive Dialog Policy Learning with Hindsight and User Modeling
Yan Cao, Keting Lu, Xiaoping Chen +1
Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences. Efficiency is of particular importance in dialog policy learni…
Learning and Reasoning for Robot Dialog and Navigation Tasks
Keting Lu, Shiqi Zhang, Peter Stone +1
Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In th…
iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots
Shiqi Zhang, Piyush Khandelwal, Peter Stone
Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning…
Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language
Yi Wang, Shiqi Zhang, Joohyung Lee
To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsens…
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Saeid Amiri, Mohammad Shokrolah Shirazi, Shiqi Zhang
Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a…
Robot Representation and Reasoning with Knowledge from Reinforcement Learning
Keting Lu, Shiqi Zhang, Peter Stone +1
Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge repr…
Goal-oriented Dialogue Policy Learning from Failures
Keting Lu, Shiqi Zhang, Xiaoping Chen
Reinforcement learning methods have been used for learning dialogue policies. However, learning an effective dialogue policy frequently requires prohibitively many conversations. T…
Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems
Yuqian Jiang, Shiqi Zhang, Piyush Khandelwal +1
Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intel…
KR: An Architecture for Knowledge Representation and Reasoning in Robotics
Shiqi Zhang, Mohan Sridharan, Michael Gelfond +1
This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason w…
Combining Answer Set Programming and POMDPs for Knowledge Representation and Reasoning on Mobile Robots
Shiqi Zhang, Mohan Sridharan
For widespread deployment in domains characterized by partial observability, non-deterministic actions and unforeseen changes, robots need to adapt sensing, processing and interact…