We study planning in a fragment of PDDL with qualitative state-trajectory constraints, capturing safety requirements, task ordering conditions, and intermediate sub-goals commonly found in real-world problems. A prominent approach to tackle such problems is to compile their constraints away, leading to a problem that is supported by state-of-the-art planners. Unfortunately, existing compilers do not scale on problems with a large number of objects and high-arity actions, as they necessitate grounding the problem before compilation. To address this issue, we propose two methods for compiling away constraints without grounding, making them suitable for large-scale planning problems. We prove the correctness of our compilers and outline their worst-case time complexity. Moreover, we present a reproducible empirical evaluation on the domains used in the latest International Planning Competition. Our results demonstrate that our methods are efficient and produce planning specifications that are orders of magnitude more succinct than the ones produced by compilers that ground the domain, while remaining competitive when used for planning with a state-of-the-art planner.

Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typically studied in isolation, complicating their comparison in terms of expressivity and obscuring the scope of their associated stream reasoners. To mitigate this issue, we map practical fragments of prominent event specification languages into Temporal Datalog→⊖, a temporal Datalog with stratified negation and no future dependencies. To support efficient stream reasoning over Temporal Datalog→⊖, we propose Streaming Trigger Graphs, an extension of a state-of-the-art technique for Datalog materialisation. Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages.

Temporal pattern matching tasks require the detection of situations of interest based on streams of symbolic events. The Run-Time Event Calculus (RTEC) is a formal framework that represents situations of interest as time-varying properties called 'fluents'. Temporal patterns often express 'Boolean combinations' of situations; RTEC features two types of fluents that may model such patterns: 'simple' and 'statically determined'. A simple fluent representation, however, is exponentially larger and more expensive to reason with than the corresponding statically determined fluent one. We formally identify the class of simple fluent definitions that can be translated into statically determined fluent definitions. Moreover, we present a compiler for the translation, and a reproducible empirical evaluation on real applications.

Composite event recognition (CER) systems detect instances of composite activities over streams of timestamped events. A fundamental operator for CER is ‘sequencing’, expressing that two activities take place one after the other. There is no consensus on a universal definition for sequencing. We provide a set of required properties for a sequencing operator for CER, i.e., an interval-based semantics, required for durative activities, and associativity, required to express activity hierarchies. We propose a sequencing operator that satisfies all requirements, as opposed to the ones in the literature, and we implement our operator in the CER engine RTEC. We compare our operator both theoretically and empirically with state-of-the-art approaches, demonstrating its benefits and limitations.

Owing to their reasoning capabilities, large language models (LLMs) have been evaluated on planning tasks described in natural language. However, LLMs have largely been tested on planning domains without constraints. In order to deploy them in real-world settings where adherence to constraints, in particular safety constraints, is critical, we need to evaluate their performance on constrained planning tasks. We introduce LexiCon—a natural language-based (Lexi) constrained (Con) planning benchmark, consisting of a suite of environments, that can be used to evaluate the planning capabilities of LLMs in a principled fashion. The core idea behind LexiCon is to take existing planning environments and impose temporal constraints on the states. These constrained problems are then translated into natural language and given to an LLM to solve. A key feature of LexiCon is its extensibility. That is, the set of supported environments can be extended with new (unconstrained) environment generators, for which temporal constraints are constructed automatically. This renders LexiCon future-proof: the hardness of the generated planning problems can be increased as the planning capabilities of LLMs improve. Our experiments reveal that the performance of state-of-the-art LLMs, including reasoning models like GPT-5, o3, and R1, deteriorates as the degree of constrainedness of the planning tasks increases.

In streaming applications, it is often required to detect situations of interest, by means of temporal pattern matching, with minimal latency. In the maritime domain, e.g., where it is crucial to prevent activities that are harmful to the environment, we need to report illegal fishing activities, based on streams of low-level vessel actions, as soon as possible. Streams often include events with delayed effects. In multi-agent voting protocols, e.g., a proposed motion may be seconded at the latest by some time in the future. In simulations of biological systems, a signal may lead to the deactivation of the functions of a gene after a time delay. We propose a formal computational framework that handles streams including events with delayed effects. We present the syntax, semantics and reasoning algorithms of our proposed framework, and demonstrate its correctness and complexity. Furthermore, we present a reproducible analysis on large synthetic and real data streams, from the fields of composite event recognition, multi-agent systems and biological feedback processes, and compare the efficiency of our approach with state-of-the-art systems that can perform stream reasoning in these domains. Our results demonstrate that our framework is capable of reasoning over very large streams, including events with delayed effects, while outperforming the state-of-the-art, often by orders of magnitude.

Contemporary applications require the processing of large, high-velocity streams of symbolic events derived from sensor data. A complex event recognition (CER) system processes these symbolic events online and reports the satisfaction of complex event patterns with minimal latency. We extend an Event Calculus dialect optimised for online CER with Allen’s interval algebra, in order to provide more accurate event patterns. We demonstrate the effectiveness of our system on real data streams from maritime situational awareness.