Domain-independent planning algorithms require as input an action model that specifies the preconditions and effects of each action. Constructing such models is often challenging for domain experts, particularly in domains where actions involve both Boolean and numeric state variables. We address the problem of learning such hybrid action models from observations of successful action executions. A central challenge is that observing only successful executions provides no explicit evidence about which conditions are necessary for an action to be applicable. This difficulty is especially pronounced for learning numeric preconditions, for which there exist negative theoretical results concerning efficient learnability. To mitigate this limitation, we propose SAM-SVM, a novel numeric action model learning algorithm that learns numeric preconditions by heuristically simulating negative observations, i.e., possible states where actions are inapplicable. We also implemented NSAM+SVM, a hybrid algorithm that integrates SAM-SVM and NSAM, an existing conservative action model learning algorithm. Empirical evaluation demonstrates that SAM-SVM can learn more accurate action models and achieves improved planning performance compared to existing methods on standard numeric planning benchmarks, and NSAM+SVM provides the most robust behavior across most domains.

Background: Multi-Agent Planning (MAP) involves coordinating the actions of multiple autonomous agents to achieve shared objectives. A prevalent formalism for MAP is the Multi-Agent Planning Domain Definition Language (MA-PDDL). While effective, existing MA-PDDL solvers typically require complete access to agents’ action models—specifically their preconditions and effects. However, manually creating these models is often intractable, requiring exhaustive domain expertise. Objectives: This work explores an alternative approach: automatically learning agents’ action models from observed transitions. Since learned models may be inaccurate, planning with them can yield invalid or non-executable sequences. To mitigate this, we formalize a requirement for safety, ensuring that plans generated via the learned model remain sound with respect to the real unknown action model. Methods: Previous research introduced the Safe Action Model Learning (SAM) algorithm for single-agent domains. However, SAM is not suitable for MA-PDDL environments where observations include concurrently executed actions, since it cannot naturally disambiguate the individual contributions of the agents to the observed effects. To address this, we introduce Multi-Agent Safe Action Model Learning (MA-SAM), a safe action model learning algorithm designed to handle concurrent multi-agent observations. For scenarios where individual action effects remain ambiguous, we further propose MA-SAM+ , which learns the preconditions and effects of macro-actions representing concurrent execution of subsets of actions. We evaluate both algorithms on domains from the Competition of Distributed and Multi-Agent Planners (CoDMAP) benchmarks and a novel MAP domain inspired by the game Overcooked. Results: We establish a theoretical lower bound on the sample complexity for learning safe action models in multi-agent settings. We prove that MA-SAM does not achieve this lower bound in all cases, identifying specific conditions under which its sample complexity may become unbounded. Empirically, both MA-SAM and MA-SAM+ significantly outperform SAM-based baselines in coverage and applicability rates. While their performance is comparable in many settings, MA-SAM+ highly outperforms MA-SAM in some of the evaluated domains. Conclusions: We present the first algorithms capable of learning safe MA-PDDL action models from concurrently executed actions, providing both theoretical foundations and empirical validation across diverse planning benchmarks

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 some planning domain description language. Manually designing such an action model is a notoriously challenging task. An alternative is to automatically learn action models from observation. Such an action model is called safe if every plan created with it is consistent with the real, unknown action model. Algorithms for learning such safe action models exist, yet they cannot handle domains with conditional or universal effects, which are common constructs in many planning problems. We prove that learning non-trivial safe action models with conditional effects may require an exponential number of samples. Then, we identify reasonable assumptions under which such learning is tractable and propose Conditional-SAM, the first algorithm capable of doing so. We analyze Conditional-SAM theoretically and evaluate it experimentally. Our results show that the action models learned by Conditional-SAM can be used to solve perfectly most of the test set problems in most of the experimented domains.

Minecraft is a widely popular video game renowned for its intricate environment. The game's open-ended design allows the creation of unique tasks and challenges for the agents, providing a broad spectrum for researchers to experiment with different AI techniques and applications. Indeed, various Minecraft tasks have been posed as an AI challenge. Most AI research on Minecraft focused on either applying Reinforcement Learning (RL) to solve the problem, learning an action model for planning, or modeling the problem for a domain-independent planner. In this work, we focus on the combinatorial search aspect of solving the Craft Wooden Pogo task within the Polycraft World AI Lab (PAL) Minecraft environment. PAL is an interface to Minecraft that provides an API for AI agents to interact with Minecraft's environment and send commands to the main character. PAL supports symbolic observations of the current state, making it ideal for planning algorithms, which require a symbolic model of the environment for problem-solving. Other Minecraft research frameworks such as MineRL, provide a visual, pixel-based representation of the game.

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 some planning domain description language. Yet obtaining such an action model is a notoriously hard task. This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. Learning safe action models for planning has been recently explored for domains in which states are sufficiently described with Boolean variables. In this work, we go beyond this limitation and propose the NSAM algorithm. NSAM runs in time that is polynomial in the number of observations and, under certain conditions, is guaranteed to return safe action models. We analyze its worst-case sample complexity, which may be intractable for some domains. Empirically, however, NSAM can quickly learn a safe action model that can solve most problems in the domain.