A Research Agenda for AI Planning in the Field of Flexible Production Systems

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived

Distinguishing between nociceptive and neuropathic components in chronic low back pain using behavioural evaluation and sensory examination

BACKGROUND: Diagnosis of chronic low back pain (CLBP) is traditionally predicated on identifying underlying pathological or anatomical causes, with treatment outcomes modest at best. Alternately, it is suggested that identification of underlying pain mechanisms with treatments targeted towards specific pain phenotypes may yield more success. Differentiation between nociceptive and neuropathic components of CLBP is problematic; evidence suggests that clinicians fail to identify a significant neuropathic component in many CLBP patients. The painDETECT questionnaire (PDQ) was specifically developed to identify occult but significant neuropathic components in individuals thought to have predominantly nociceptive pain. METHODS: Using the PDQ, we classified 50 CLBP patients into two distinct groups; those with predominantly nociceptive pain (Group 1) and those with a significant neuropathic component (Group 2). We characterised these two distinct CLBP sub-groups using a) questionnaire-based behavioural evaluation measuring pain-related function and quality of life, pain intensity and psychological well-being and b) sensory examination, using two-point and tactile threshold discrimination. OBJECTIVE: We sought to determine if differences in the pain phenotype of each CLBP sub-group would be reflected in sensory and behavioural group profiles. RESULTS: We report that Group 1 and Group 2 sub-groups demonstrate unique clinical profiles with significant differences in sensory tactile discrimination thresholds and in a wide range of behavioural domains measuring pain intensity, disability and psychological well-being. CONCLUSION: We have demonstrated distinct clinical profiles for CLBP patient sub-groups classified by PDQ. Our results give diagnostic confidence in using the PDQ to characterise two distinct pain phenotypes in a heterogeneous CLBP population