Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

In Automated Planning, learning and exploiting additional knowledge within a domain model, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising because they are to some extent domain model and planning engine independent. This paper aims to exploit recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate achievements or requirements', for generating macro-operators. We discuss conditions which are necessary for generating such macro-operators and conditions that allow removing primitive operators without compromising solvability of a given (class of) problem(s). The effectiveness of our approach will be experimentally shown on a set of well-known benchmark domains using several high-performing planning engines.

Analysing the structures of solution plans generated by AI Planning engines is helpful in improving the generative planning process, as well as shedding light in the study of its theoretical foundations. We investigate a specific property of solution plans, that we called linearity, which refers to a situation where each action achieves an atom (or atoms) for a directly following action, or achieves goal atom(s). Similarly, linearity can be defined for parallel plans where each action in a set of actions executed at some time step, achieves either goal atom(s) or atom(s) for some action executed in the directly following time step. In this paper, we present a general and problem-independent theoretical framework focusing on the analysis of planning operator schema, namely relations of achiever, clobberer and independence, in order to determine whether solvable planning problems using a given operator schema have as solutions optimal (parallel) plans which are linear. The findings presented in this paper deepen current theoretical knowledge, provide helpful information to engineers of new planning domain models, and suggest new ways of improving the performance of state-of-the-art (optimal) planning engines.

Domain-independent planning requires only to specify planning problems in a standard language (e.g.

Macro-operator (macro, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training.

Capturing and exploiting structural knowledge of planning problems has shown to be a successful strategy for making the planning process more efficient. Plans can be decomposed into its constituent coherent subplans, called blocks, that encapsulate some effects and preconditions, reducing interference and thus allowing more deordering of plans. According to the nature of blocks, they can be straightforwardly transformed into useful macro-operators (shortly, macros). Macros are well known and widely studied kind of structural knowledge because they can be easily encoded in the domain model and thus exploited by standard planning engines. In this paper, we introduce a method, called BloMa, that learns domain-specific macros from plans, decomposed into macro-blocks which are extensions of blocks, utilising structural knowledge they capture. In contrast to existing macro learning techniques, macro-blocks are often able to capture high-level activities that form a basis for useful longer macros (i.e.

Effective and efficient reasoning in adversarial environments is important for many real-world applications ranging from cybersecurity to military operations. Deliberative reasoning techniques, such as Automated Planning, often restrict to static environments where only an agent can make changes by its actions. On the other hand, such techniques are effective and can generate non-trivial solutions. To explicitly reason in environments with an active adversary such as zero-sum games, the game-theoretic framework such as the Double Oracle algorithm can be leveraged. In this paper, we leverage the notions of critical and adversary actions, where critical actions should be applied before the adversary ones. We propose heuristics that provide a guidance for planners about what (critical) actions and in which order have to be applied in a good plan.