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Presented at KEPS 2022 Workshop on Knowledge Engineering for Planning and Scheduling at the 32nd International Conference on Automated Planning and Scheduling (ICAPS). Abstract As individual sub-fields of AI become more developed, it becomes increasingly important to study their integration into complex systems. In this paper, we look at three examples of how automated planning can be integrated with learning and reasoning. We then provide a first look at the AI Domain Definition Language (AIDDL) as an attempt to provide a common ground for modeling problems, data, solutions, and their integration across all branches of AI in a common language. &nbsp

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

We describe an approach for integrating ontologies with a constraint-based planner to compile configuration planning domains based on the current context. We consider two alternative approaches: The first one integrates SPARQL queries directly with the planner while the second one generates SPARQL queries dynamically from provided triples. The first approach offers the full freedom of the SPARQL query language, while the second offers a more dynamic way for the planner to influence queries based on what is currently relevant for the planner. We evaluate the approach based on how much redundancy is removed by “outsourcing” knowledge into the ontology compared to modeling it directly into the domain of the planner.

This paper describes the design and implementationof a network scheduler prototype for IoT networks within the e-healthcare domain. The network scheduler combines a constraint-based task planner with the Lightweight Machine-to-Machine (LwM2M) protocol to be able to reconfigure IoT networks at run-time based on recognized activities and changes in the environment. To support such network scheduling, we implement a LwM2M application layer for the IoT devices that provides sensor data, network stack information, and a set of controllable parameters that affect the communication performance and the energy consumption.

As more robots and sensors are deployed in work and home environments, there is a growing need for these devices to act with some degree of autonomy to fulfill their purpose. Automated planning can be used to synthesize plans of action that achieve this. The main challenge addressed in this thesis is to consider how the automated planning problem changes when considered in the context of environments that are populated by humans. Humans have their own plans, and automatically generated plans should not interfere with these. We refer to this as social acceptability. Opportunities for proactive behavior often arise during execution. The planner should be able to identify these opportunities and proactively plan accordingly. Both social acceptability and proactivity require the planner to identify relevant situations from available information. We refer to this capability as context-awareness, and it may require complex inferences based on observed human activities. Finally, planning may have to consider cooperation with humans to reach common goals or to enable robots and humans to support one another. This thesis analyzes the requirements that emerge from human-aware planning — what it takes to make automated planning socially acceptable, proactive, context aware, and to make it support cooperation with humans. We formally state the human-aware planning problem, and propose a planning and execution framework for human-aware planning that is based on constraint reasoning and flaw-resolution techniques, and which fulfills the identified requirements. This approach is modular and extendable: new types of constraints can be added and solvers can be exchanged and re-arranged. This allows us to address the identified requirements for humanaware planning. In particular, we introduce Interaction Constraints (ICs) for this purpose, and propose patterns of Ics for social acceptability, proactivity, and contextawareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. introduce Interaction Constraints (ICs) for this purpose, and propose patterns of ICs for social acceptability, proactivity, and context-awareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system.