3 citations · 6 across the 13 of their papers we have counts for
16 papers
Constrained Multi-Agent Reinforcement Learning in Imagination
Raz Shmueli, Shahaf S. Shperberg
Predicting cost in a multi-agent world model moves most constrained policy updates into imagination: the stronger model-free baseline needs to times CA-MAMBA's interactio…
Optimal Planning in a Dynamic World
Devin Wild Thomas, Solomon Eyal Shimony, Wheeler Ruml +3
Background: We address the problem of planning when the set of feasible states or actions changes over time. For example, in the problem of path planning among moving obstacles (so…
StochSIPP: Safe Interval Path Planning in Stochastic Dynamic Environments
Ajith Kemisetti, Shahaf S. Shperberg, Yoonchang Sung
Safe navigation under uncertain time-dependent blockage requires anticipating observations before committing to motion. We present StochSIPP, an exact contingent planner for tempor…
Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics
Tzur Shubi, Ariel Felner, Solomon Eyal Shimony +1
Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search. Therein, it is well-known that front-to-front heuristics can reduce th…
RAMP: Hybrid DRL for Online Learning of Numeric Action Models
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Automated planning algorithms require an action model specifying the preconditions and effects of each action, but obtaining such a model is often hard. Learning action models from…
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequenc…
Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics
Shahaf S. Shperberg, Natalie Morad, Lior Siag +2
Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal searc…
Beyond Single-Step Updates: Reinforcement Learning of Heuristics with Limited-Horizon Search
Gal Hadar, Forest Agostinelli, Shahaf S. Shperberg
Many sequential decision-making problems can be formulated as shortest-path problems, where the objective is to reach a goal state from a given starting state. Heuristic search is…
Toward PDDL Planning Copilot
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Large Language Models (LLMs) are increasingly being used as autonomous agents capable of performing complicated tasks. However, they lack the ability to perform reliable long-horiz…
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Automated Planning algorithms require a model of the domain that specifies the preconditions and effects of each action. Obtaining such a domain model is notoriously hard. Algorith…
On Parallel External-Memory Bidirectional Search
Lior Siag, Shahaf S. Shperberg, Ariel Felner +1
Parallelization and External Memory (PEM) techniques have significantly enhanced the capabilities of search algorithms when solving large-scale problems. Previous research on PEM h…
Effort Allocation for Deadline-Aware Task and Motion Planning: A Metareasoning Approach
Yoonchang Sung, Shahaf S. Shperberg, Qi Wang +1
In robot planning, tasks can often be achieved through multiple options, each consisting of several actions. This work specifically addresses deadline constraints in task and motio…
Planning and Acting While the Clock Ticks
Andrew Coles, Erez Karpas, Andrey Lavrinenko +3
Standard temporal planning assumes that planning takes place offline and then execution starts at time 0. Recently, situated temporal planning was introduced, where planning starts…
Learning Safe Numeric Planning Action Models
Argaman Mordoch, Shahaf S. Shperberg, Roni Stern +1
A significant challenge in applying planning technology to real-world problems lies in obtaining a planning model that accurately represents the problem's dynamics. Obtaining a pla…
A Formal Metareasoning Model of Concurrent Planning and Execution
Amihay Elboher, Ava Bensoussan, Erez Karpas +3
Agents that plan and act in the real world must deal with the fact that time passes as they are planning. When timing is tight, there may be insufficient time to complete the searc…
Learning a Shield from Catastrophic Action Effects: Never Repeat the Same Mistake
Shahaf S. Shperberg, Bo Liu, Peter Stone
Agents that operate in an unknown environment are bound to make mistakes while learning, including, at least occasionally, some that lead to catastrophic consequences. When humans…