Planning is a pivotal ability of any intelligent system being developed for real-world applications. AI planning is concerned with researching and developing planning systems that automatically compute plans that satisfy some user objective. Identifying and understanding the relevant and realistic aspects that characterise real-world application domains are crucial to the development of AI planning systems. This provides guidance to knowledge engineers and software engineers in the process of designing, identifying, and categorising resources required for the development process. To the best of our knowledge, such support does not exist. We address this research gap by developing a conceptual framework that identifies and categorises the aspects of real-world planning domains in varying levels of granularity. Our framework provides not only a common terminology but also a comprehensive overview of a broad range of planning aspects exemplified using the domain of sustainable buildings as a prominent application domain of AI planning. The framework has the potential to impact the design, development, and applicability of AI planning systems in real-world application domains.

In this paper, we introduce an approach to validate the functional equivalence of planning domain models. Validating the functional equivalence of planning domain models is the problem of formally confirming that two planning domain models can be used to solve the same set of problems. The need for techniques to validate the functional equivalence of planning domain models has been highlighted in previous research and has applications in model learning, development and extension. We prove the soundness and completeness of our method. We also develop D-VAL, an automatic functional equivalence validation tool for planning domain models. Empirical evaluation shows that D-VAL validates the functional equivalence of most examined domains in less than five minutes. Additionally, we provide a benchmark to evaluate the feasibility and scalability of this and future related work.

Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in the environment. Existing approaches assume that the possible goals are formalized as a conjunction in deterministic settings. In this paper, we develop a novel approach that is capable of recognizing temporally extended goals in Fully Observable Non-Deterministic (FOND) planning domain models, focusing on goals on finite traces expressed in Linear Temporal Logic (LTLf) and (Pure) Past Linear Temporal Logic (PLTLf). We empirically evaluate our goal recognition approach using different LTLf and PLTLf goals over six common FOND planning domain models, and show that our approach is accurate to recognize temporally extended goals at several levels of observability.

The verification of planning domain models is crucial to ensure the safety, integrity and correctness of planning-based automated systems. This task is usually performed using model checking techniques. However, directly applying model checkers to verify planning domain models can result in false positives, i.e. counterexamples that are unreachable by a sound planner when using the domain under verification during a planning task. In this paper, we discuss the downside of unconstrained planning domain model verification. We then propose a fail-safe practice for designing planning domain models that can inherently guarantee the safety of the produced plans in case of undetected errors in domain models. In addition, we demonstrate how model checkers, as well as state trajectory constraints planning techniques, should be used to verify planning domain models so that unreachable counterexamples are not returned.

The development of domain-independent planners This modular approach also supports the use of reformulation within the AI Planning community is leading to and configuration techniques which can automatically "off the shelf" technology that can be used in a reformulate, re-represent or tune the domain model and/or wide range of applications. Moreover, it allows a problem description in order to increase the efficiency of modular approach - in which planners and domain a planner and increase the scope of problems solved. The knowledge are modules of larger software applications idea is to make these techniques to some degree independent - that facilitates substitutions or improvements of domain and planner (that is, applicable to a range of individual modules without changing the of domains and planning engine technologies), and use them rest of the system. This approach also supports the to form a wrapper around a planner, improving its overall use of reformulation and configuration techniques, performance for the domain to which it is applied. Types which transform how a model is represented in order of reformulation include macro-learning [Botea et al., 2005; to improve the efficiency of plan generation. Newton et al., 2007], action schema splitting [Areces et al., In this paper, we investigate how the performance 2014] and entanglements [Chrpa and McCluskey, 2012]: here of planners is affected by domain model configuration.