arXiv:2606.16329v1 Announce Type: new Abstract: In this paper, we present a procedure for numeric planning based on Symbolic Pattern Planning (SPP). Given a numeric planning problem $\Pi$, a pattern $\prec$ is a sequence of actions used to define a formula encoding the subsequences of $\prec$ executable from a starting state $S$. Cardellini, Giunchiglia, and Maratea (2024a) follow the Planning as Satisfiability approach by defining, at each step $n \ge 0$, a formula $\Pi^\prec_n$ in which $(i)$ the pattern $\prec$ is computed only for $n=0$ in the initial state $I$ of $\Pi$, and then exploited
The paper presents a new procedure for numeric planning, demonstrating a continued focus on improving AI's ability to handle complex, real-world problems with both symbolic and numeric reasoning.
This research contributes to the foundational capabilities of AI, potentially leading to more sophisticated and autonomous systems that can navigate and solve problems in dynamic environments.
The proposed method could enhance the efficiency and practicality of AI planning, accelerating the development of agentic systems capable of more complex decision-making and action sequencing.
- · AI research institutions
- · Robotics developers
- · Logistics and automation sectors
- · AI agent developers
- · manual labor in planning-heavy industries
- · prior less efficient symbolic numeric planning methods
Improved AI planning algorithms will enable more robust and advanced autonomous systems.
These systems could automate increasingly complex tasks across various industries, from manufacturing to resource management.
The widespread deployment of highly capable AI agents might accelerate the transition towards a more automated economy, impacting labor markets and operational efficiencies on a large scale.
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Read at arXiv cs.AI