On the Complexity of Verifying Timed Golog Programs over Description Logic Actions: Extended Version

Golog programs allow to model complex behaviour of agents by combining primitive actions defined in a Situation Calculus theory using imperative and non-deterministic programming language constructs. In general, verifying temporal properties of Golog programs is undecidable. One way to establish decidability is to restrict the logic used by the program to a Description Logic (DL), for which recently some complexity upper bounds for verification problem have been established. However, so far it was open whether these results are tight, and lightweight DLs such as EL have not been studied at all. Furthermore, these results only apply to a setting where actions do not consume time, and the properties to be verified only refer to the timeline in a qualitative way. In a lot of applications, this is an unrealistic assumption. In this work, we study the verification problem for timed Golog programs, in which actions can be assigned differing durations, and temporal properties are specified in a metric branching time logic. This allows to annotate temporal properties with time intervals over which they are evaluated, to specify for example that some property should hold for at least n time units, or should become specified within some specified time window. We establish tight complexity bounds of the verification problem for both expressive and lightweight DLs. Our lower bounds already apply to a very limited fragment of the verification problem, and close open complexity bounds for the non-metrical cases studied before

Towards Bridging the Gap between High-Level Reasoning and Execution on Robots

When reasoning about actions, e.g., by means of task planning or agent programming with Golog, the robot's actions are typically modeled on an abstract level, where complex actions such as picking up an object are treated as atomic primitives with deterministic effects and preconditions that only depend on the current state. However, when executing such an action on a robot it can no longer be seen as a primitive. Instead, action execution is a complex task involving multiple steps with additional temporal preconditions and timing constraints. Furthermore, the action may be noisy, e.g., producing erroneous sensing results and not always having the desired effects. While these aspects are typically ignored in reasoning tasks, they need to be dealt with during execution. In this thesis, we propose several approaches towards closing this gap.Comment: PhD Thesi

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Under consideration for publication in Theory and Practice of Logic Programming ALPprolog -A New Logic Programming Method for Dynamic Domains

Abstract Logic programming is a powerful paradigm for programming autonomous agents in dynamic domains, as witnessed by languages such as Golog and Flux. In this work we present ALPprolog, an expressive, yet efficient, logic programming language for the online control of agents that have to reason about incomplete information and sensing actions

Interpretable task planning and learning for autonomous robotic surgery with logic programming

This thesis addresses the long-term goal of full (supervised) autonomy in surgery, characterized by dynamic environmental (anatomical) conditions, unpredictable workflow of execution and workspace constraints. The scope is to reach autonomy at the level of sub-tasks of a surgical procedure, i.e. repetitive, yet tedious operations (e.g., dexterous manipulation of small objects in a constrained environment, as needle and wire for suturing). This will help reducing time of execution, hospital costs and fatigue of surgeons during the whole procedure, while further improving the recovery time for the patients. A novel framework for autonomous surgical task execution is presented in the first part of this thesis, based on answer set programming (ASP), a logic programming paradigm, for task planning (i.e., coordination of elementary actions and motions). Logic programming allows to directly encode surgical task knowledge, representing emph{plan reasoning methodology} rather than a set of pre-defined plans. This solution introduces several key advantages, as reliable human-like interpretable plan generation, real-time monitoring of the environment and the workflow for ready adaptation and failure recovery. Moreover, an extended review of logic programming for robotics is presented, motivating the choice of ASP for surgery and providing an useful guide for robotic designers. In the second part of the thesis, a novel framework based on inductive logic programming (ILP) is presented for surgical task knowledge learning and refinement. ILP guarantees fast learning from very few examples, a common drawback of surgery. Also, a novel action identification algorithm is proposed based on automatic environmental feature extraction from videos, dealing for the first time with small and noisy datasets collecting different workflows of executions under environmental variations. This allows to define a systematic methodology for unsupervised ILP. All the results in this thesis are validated on a non-standard version of the benchmark training ring transfer task for surgeons, which mimics some of the challenges of real surgery, e.g. constrained bimanual motion in small space

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

Action Logic Programs: How to Specify Strategic Behavior in Dynamic Domains Using Logical Rules

We discuss a new concept of agent programs that combines logic programming with reasoning about actions. These agent logic programs are characterized by a clear separation between the specification of the agent’s strategic behavior and the underlying theory about the agent’s actions and their effects. This makes it a generic, declarative agent programming language, which can be combined with an action representation formalism of one’s choice. We present a declarative semantics for agent logic programs along with (two versions of) a sound and complete operational semantics, which combines the standard inference mechanisms for (constraint) logic programs with reasoning about actions