Decidable Reasoning About Time in Finite-Domain Situation Calculus Theories

Representing time is crucial for cyber-physical systems and has been studied extensively in the Situation Calculus. The most commonly used approach represents time by adding a real-valued fluent $\mathit{time}(a)$ that attaches a time point to each action and consequently to each situation. We show that in this approach, checking whether there is a reachable situation that satisfies a given formula is undecidable, even if the domain of discourse is restricted to a finite set of objects. We present an alternative approach based on well-established results from timed automata theory by introducing clocks as real-valued fluents with restricted successor state axioms and comparison operators. %that only allow comparisons against fixed rationals. With this restriction, we can show that the reachability problem for finite-domain basic action theories is decidable. Finally, we apply our results on Golog program realization by presenting a decidable procedure for determining an action sequence that is a successful execution of a given program.

Extracting Rules from Event Data for Study Planning

In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students. The main goal is to offer valuable guidance for their study planning. We employ process and data mining techniques to explore the impact of sequences of taken courses on academic success. Through the use of decision tree models, we generate data-driven recommendations in the form of rules for study planning and compare them to the recommended study plan. The evaluation focuses on RWTH Aachen University computer science bachelor program students and demonstrates that the proposed course sequence features effectively explain academic performance measures. Furthermore, the findings suggest avenues for developing more adaptable study plans.

Multi-Valued Partial Order Plans in Numeric Planning

Many planning formalisms allow for mixing numeric with Boolean effects. However, most of these formalisms are undecidable. In this paper, we will analyze possible causes for this undecidability by studying the number of different occurrences of actions, an approach that proved useful for metric fluents before. We will start by reformulating a numeric planning problem known as restricted tasks as a search problem. We will then show how an NP-complete fragment of numeric planning can be found by using heuristics. To achieve this, we will develop the idea of multi-valued partial order plans, a least committing compact representation for (sequential and parallel) plans. Finally, we will study optimization techniques for this representation to incorporate soft preconditions.

Digital Shadows of Safety for Human Robot Collaboration in the World-Wide Lab

The World Wide Lab (WWL) connects the Digital Shadows (DSs) of processes, products, companies, and other entities allowing the exchange of information across company boundaries. Since DSs are context- and purpose-specific representations of a process, as opposed to Digital Twins (DTs) which offer a full simulation, the integration of a process into the WWL requires the creation of DSs representing different aspects of the process. Human-Robot Collaboration (HRC) for assembly processes was recently studied in the context of the WWL where Behaviour Trees (BTs) were proposed as a standard task-level representation of these processes. We extend previous work by proposing to standardise safety functions that can be directly integrated into these BTs. This addition uses the WWL as a communication and information exchange platform allowing industrial and academic practitioners to exchange, reuse, and experiment with different safety requirements and solutions in the WWL.

Assistive Robot Teleoperation Using Behavior Trees

Robotic assistance in robot arm teleoperation tasks has recently gained a lot of traction in industrial and domestic environment. A wide variety of input devices is used in such setups. Due to the noise in the input signals (e.g., Brain Computer Interface (BCI)) or delays due to environmental conditions (e.g., space robot teleoperation), users need assistive autonomy that keeps them in control while following predefined trajectories and avoids obstacles. This assistance calls for activity representations that are easy to define by the operator and able to take the dynamic world state into consideration. This paper represents Activities of Daily Living using Behavior Trees (BTs) whose inherent readability and modularity enables an end user to define new activities using a simple interface. To achieve this, we augment BTs with Shared Control Action Nodes, which guide the user's input on a trajectory facilitating and ensuring task execution.

A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.

Predictive Object-Centric Process Monitoring

The automation and digitalization of business processes has resulted in large amounts of data captured in information systems, which can aid businesses in understanding their processes better, improve workflows, or provide operational support. By making predictions about ongoing processes, bottlenecks can be identified and resources reallocated, as well as insights gained into the state of a process instance (case). Traditionally, data is extracted from systems in the form of an event log with a single identifying case notion, such as an order id for an Order to Cash (O2C) process. However, real processes often have multiple object types, for example, order, item, and package, so a format that forces the use of a single case notion does not reflect the underlying relations in the data. The Object-Centric Event Log (OCEL) format was introduced to correctly capture this information. The state-of-the-art predictive methods have been tailored to only traditional event logs. This thesis shows that a prediction method utilizing Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM) architectures, and Sequence to Sequence models (Seq2seq), can be augmented with the rich data contained in OCEL. Objects in OCEL can have attributes that are useful in predicting the next event and timestamp, such as a priority class attribute for an object type package indicating slower or faster processing. In the metrics of sequence similarity of predicted remaining events and mean absolute error (MAE) of the timestamp, the approach in this thesis matches or exceeds previous research, depending on whether selected object attributes are useful features for the model. Additionally, this thesis provides a web interface to predict the next sequence of activities from user input.

Towards Using Promises for Multi-Agent Cooperation in Goal Reasoning

Reasoning and planning for mobile robots is a challenging problem, as the world evolves over time and thus the robot's goals may change. One technique to tackle this problem is goal reasoning, where the agent not only reasons about its actions, but also about which goals to pursue. While goal reasoning for single agents has been researched extensively, distributed, multi-agent goal reasoning comes with additional challenges, especially in a distributed setting. In such a context, some form of coordination is necessary to allow for cooperative behavior. Previous goal reasoning approaches share the agent's world model with the other agents, which already enables basic cooperation. However, the agent's goals, and thus its intentions, are typically not shared. In this paper, we present a method to tackle this limitation. Extending an existing goal reasoning framework, we propose enabling cooperative behavior between multiple agents through promises, where an agent may promise that certain facts will be true at some point in the future. Sharing these promises allows other agents to not only consider the current state of the world, but also the intentions of other agents when deciding on which goal to pursue next. We describe how promises can be incorporated into the goal life cycle, a commonly used goal refinement mechanism. We then show how promises can be used when planning for a particular goal by connecting them to timed initial literals (TILs) from PDDL planning. Finally, we evaluate our prototypical implementation in a simplified logistics scenario.

On the Verification of Belief Programs

In a recent paper, Belle and Levesque proposed a framework for a type of program called belief programs, a probabilistic extension of GOLOG programs where every action and sensing result could be noisy and every test condition refers to the agent's subjective beliefs. Inherited from GOLOG programs, the action-centered feature makes belief programs fairly suitable for high-level robot control under uncertainty. An important step before deploying such a program is to verify whether it satisfies properties as desired. At least two problems exist in doing verification: how to formally specify properties of a program and what is the complexity of verification. In this paper, we propose a formalism for belief programs based on a modal logic of actions and beliefs. Among other things, this allows us to express PCTL-like temporal properties smoothly. Besides, we investigate the decidability and undecidability for the verification problem of belief programs.

Ontology-Assisted Generalisation of Robot Action Execution Knowledge

When an autonomous robot learns how to execute actions, it is of interest to know if and when the execution policy can be generalised to variations of the learning scenarios. This can inform the robot about the necessity of additional learning, as using incomplete or unsuitable policies can lead to execution failures. Generalisation is particularly relevant when a robot has to deal with a large variety of objects and in different contexts. In this paper, we propose and analyse a strategy for generalising parameterised execution models of manipulation actions over different objects based on an object ontology. In particular, a robot transfers a known execution model to objects of related classes according to the ontology, but only if there is no other evidence that the model may be unsuitable. This allows using ontological knowledge as prior information that is then refined by the robot's own experiences. We verify our algorithm for two actions - grasping and stowing everyday objects - such that we show that the robot can deduce cases in which an existing policy can generalise to other objects and when additional execution knowledge has to be acquired.