1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generation
Lior Broide, Roni Stern, Argaman Mordoch
Search-Based Software Testing (SBST) is a well-established approach for automated unit test generation, yet it often suffers from premature convergence and limited diversity in the…
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…
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…
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…
Safe Learning of PDDL Domains with Conditional Effects -- Extended Version
Argaman Mordoch, Enrico Scala, Roni Stern +1
Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in s…