In this demonstration, we present a system that automates regression testing for video games using automated planning techniques. Traditional test scripts are a common method for testing both video games and software in general. While effective, they require manual creation and frequent updates throughout development, making the process labor-intensive. Our system eliminates this burden by automatically generating and maintaining test scripts. The test engineer only needs to define the game’s rules using the Planning Domain Definition Language (PDDL) and specify initial states and goals for individual test cases. This significantly reduces human effort while ensuring test scripts remain up to date. Additionally, our system integrates with game engine editors—supporting both Unity and Unreal to execute and evaluate test cases directly within the game. It collects detailed logs, telemetry data, and video recordings, allowing users to review test results efficiently.

Existing planning action domain model acquisition approaches consider different types of state traces from which they learn. The differences in state traces refer to the level of observability of state changes (from full to none) and whether the observations have some noise (the state changes might be inaccurately logged). However, to the best of our knowledge, all the existing approaches consider state traces in which each state change corresponds to an action specified by its name and all its parameters (all objects that are relevant to the action). Furthermore, the names and types of all the parameters of the actions to be learned are given. These assumptions are too strong. In this paper, we propose a method that learns action schema from state traces with fully observable state changes but without the parameters of actions responsible for the state changes (only action names are part of the state traces). Although we can easily deduce the number (and names) of the actions that will be in the learned domain model, we still need to deduce the number and types of the parameters of each action alongside its precondition and effects. We show that this task is at least as hard as graph isomorphism. However, our experimental evaluation on a large collection of IPC benchmarks shows that our approach is still practical as the number of required parameters is usually small. Compared to the state-of-the-art learning tools SAM and Extended SAM our new algorithm can provide better results in terms of learning action models more similar to reference models, even though it uses less information and has fewer restrictions on the input traces.

AI Planning is inherently hard and hence it is desirable to derive as much information as we can from the structure of the planning problem and let this information be exploited by a planner. Many recent planners use the finite-domain state-variable representation of the problem instead of the classical propositional representation. However, most planning problems are still specified in the propositional representation due to the widespread modelling language planning domain definition language and it is hard to generate an efficient state-variable representation from the propositional model. In this article, we investigate various methods for automated generation of efficient state-variable representations from the propositional representation and we propose a novel approach – constructed as a combination of existing techniques – that utilises the structural information from the goal and the initial state. We perform an exhaustive experimental evaluation of methods, planning systems and problems, using the International Planning Competition as the main source of data. We show that for many planning problems the novel approach provides an improved efficiency.

This paper reports on FAPE (Flexible Acting and Planning Environment), a framework integrating acting and planning on the basis of the ANML modeling language. ANML is a recent proposal motivated by combining the expressiveness of the timeline representation with decomposition methods of Hierarchical Task Networks (HTN). Our current focus is not efficient temporal planning per se, but the tight integration of acting and planning. This integration is addressed by: (i) extending HTN methods with the refinement of planned actions with skills, expressed in PRS, to map actions into low-level commands, (ii) interleaving the planning process with acting, the former performs plan repair and replanning, while the latter implements the skill-based refinements, and (iii) executing commands with a dispatching mechanism that synchronizes observed time points of action effects and events with planned time. FAPE has been integrated to a PR2 robot and experimented in a home-like environment. The paper presents how planning is performed and integrated with acting and describes briefly the robotics experiments.

The Petrobras domain is an abstraction of a real-life problem of resource-efficient transportation of goods from ports to petroleum platforms. Being a good example of a difficult problem standing on the borderline between planning and scheduling, this domain was proposed as a challenge problem at the International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS 2012). In this paper we describe three different ways of modeling and solving this domain: by utilizing classical planning, temporal planning, and finally, single-player games and Monte-Carlo Tree Search.

AI Planning typically deals with the causal relations between the actions while the role of explicit time and limited resources is suppressed. The recent trends show that integrating time and resource reasoning into planning significantly improves direct applicability of planning technology in real-life problems. In this paper we propose a suboptimal domain-independent planning system Filuta that focuses on planning, where explicit time plays a major role and resources are constrained. We benchmark Filuta on the planning problems from the International Planning Competition (IPC) 2008 and compare our results with the competition participants.