1 citations · 2 across the 6 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…
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…
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…
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…
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…