A decision-making approach to the operation of flexible manufacturing systems

This paper introduces a generic decision-making framework for assigning resources of a manufacturing system to production tasks. Resources are broadly defined production units, such as machines, human operators, or material handling vehicles; and tasks are activities performed by resources. In the specific context of FMS, resources correspond to individual machines; tasks correspond to operations to be performed on parts. The framework assumes a hierarchical structure of the system and calls for the execution of four consecutive steps to make a decision for the assignment of a resource to a task. These steps are 1) establishment of decision-making criteria, 2) formation of alternative assignments, 3) estimation of the consequences of the assignments, and 4) selection of the best alternative assignment. This framework has been applied to an existing FMS as an operational policy that decides what task will be executed on which resource of this FMS. Simulation runs provide some initial results of the application of this policy. It is shown that the policy provides flexibility in terms of system performance and computational effort.     

Case-Based Learning: Predictive Features in Indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This predictive features hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.We present a specific case-based model of plan execution, RUNNER, along with its indices for recognizing when to select particular plans—appropriateness conditions—and how these predictive indices serve to enhance learning. We then discuss how this predictive features claim as implemented in the RUNNER model is then tested in a second set of psychological studies. The results show that learning appropriateness conditions results in greater success in recognizing when a past plan is in fact relevant in current processing, and produces more reliable recall of the related information. This form of collaboration has resulted in a unique integration of computational and empirical efforts to create a model of case-based learning.     

Formalizing process-based risk with Value-Focused Process Engineering

Following calls to advance the integration of risk and business process modeling paradigms, this paper formalizes the process of incorporating risk into business process models through the principles of Value-Focused Process Engineering (VFPE). In doing so, the paper aims to extend the existing VFPE modeling notation to reflect a set of necessary constructs required to adequately represent risk in goal-oriented business-process models. The extended set of constructs is proposed to support a formal systems view of process-based risk. Process-based risk is formalized on the one hand, as a product of complex interactions between activity-based elements, and on the other hand, as a natural component of the value creation mechanism of an elementary function or a complex process. The proposed risk-aware VFPE formalism also formulates rules for decomposing risk in process models according to the organizational values, thereby enabling better risk visibility, reducing process complexity, and ensuring continuity of business processes.     

Decision-theoretic planning with generalized first-order decision diagrams

Many tasks in AI require representation and manipulation of complex functions. First-Order Decision Diagrams (FODD) are a compact knowledge representation expressing functions over relational structures. They represent numerical functions that, when constrained to the Boolean range, use only existential quantification. Previous work has developed a set of operations for composition and for removing redundancies in FODDs, thus keeping them compact, and showed how to successfully employ FODDs for solving large-scale stochastic planning problems through the formalism of relational Markov decision processes (RMDP). In this paper, we introduce several new ideas enhancing the applicability of FODDs. More specifically, we first introduce Generalized FODDs (GFODD) and composition operations for them, generalizing FODDs to arbitrary quantification. Second, we develop a novel approach for reducing (G)FODDs using model checking. This yields – for the first time – a reduction that maximally reduces the diagram for the FODD case and provides a sound reduction procedure for GFODDs. Finally we show how GFODDs can be used in principle to solve RMDPs with arbitrary quantification, and develop a complete solution for the case where the reward function is specified using an arbitrary number of existential quantifiers followed by an arbitrary number of universal quantifiers.     

TimeLapseAnalyzer: multi-target analysis for live-cell imaging and time-lapse microscopy

The direct observation of cells over time using time-lapse microscopy can provide deep insights into many important biological processes. Reliable analyses of motility, proliferation, invasive potential or mortality of cells are essential to many studies involving live cell imaging and can aid in biomarker discovery and diagnostic decisions. Given the vast amount of image- and time-series data produced by modern microscopes, automated analysis is a key feature to capitalize the potential of time-lapse imaging devices. To provide fast and reproducible analyses of multiple aspects of cell behaviour, we developed TimeLapseAnalyzer. Apart from general purpose image enhancements and segmentation procedures, this extensible, self-contained, modular cross-platform package provides dedicated modalities for fast and reliable analysis of multi-target cell tracking, scratch wound healing analysis, cell counting and tube formation analysis in high throughput screening of live-cell experiments. TimeLapseAnalyzer is freely available (MATLAB, Open Source) at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/tla.     

Using Latent Trait Modeling to Conceptualize an Alcohol Problems Continuum.

Recent research points toward the viability of conceptualizing alcohol problems as arrayed along a continuum. Nevertheless, modern statistical techniques designed to scale multiple problems along a continuum (latent trait modeling; LTM) have rarely been applied to alcohol problems. This study applies LTM methods to data on 110 problems reported during in-person interviews of 1,348 middle-aged men (mean age=43) from the general population. The results revealed a continuum of severity linking the 110 problems, ranging from heavy and abusive drinking, through tolerance and withdrawal, to serious complications of alcoholism. These results indicate that alcohol problems can be arrayed along a dimension of severity and emphasize the relevance of LTM to informing the conceptualization and assessment of alcohol problems. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Identifying Psychopathy Subtypes on the Basis of Personality Structure.

The authors used model-based cluster analysis to identify subtypes of criminal psychopaths on the basis of differences in personality structure. Participants included 96 male prisoners diagnosed as psychopathic, using the Psychopathy Checklist Revised (PCL-R; R. D. Hare, 1991). Personality was assessed using the brief form of the Multidimensional Personality Questionnaire (MPQ-BF; C. J. Patrick, J. J. Curtin, & A. Tellegen, 2002). The best-fitting model yielded two clusters. Emotionally stable psychopaths were characterized by low Stress Reaction and high Agency. Aggressive psychopaths were characterized by high Negative Emotionality, low Constraint, and low Communion. These results suggest that psychopaths as defined by the PCL-R includes distinct subtypes, distinguishable in terms of personality structure, that may reflect different etiologies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Two Subgroups within the GH43_36 α-l-Arabinofuranosidase Subfamily Hydrolyze Arabinosyl from Either Mono-or Disubstituted Xylosyl Units in Wheat Arabinoxylan

Fungal arabinofuranosidases (ABFs) catalyze the hydrolysis of arabinosyl substituents (Ara) and are key in the interplay with other glycosyl hydrolases to saccharify arabinoxylans (AXs). Most characterized ABFs belong to GH51 and GH62 and are known to hydrolyze the linkage of α-(1→2)-Ara and α-(1→3)-Ara in monosubstituted xylosyl residues (Xyl) (ABF-m2,3). Nevertheless, in AX a substantial number of Xyls have two Aras (i.e., disubstituted), which are unaffected by ABFs from GH51 and GH62. To date, only two fungal enzymes have been identified (in GH43_36) that specifically release the α-(1→3)-Ara from disubstituted Xyls (ABF-d3). In our research, phylogenetic analysis of available GH43_36 sequences revealed two major clades (GH43_36a and GH43_36b) with an expected substrate specificity difference. The characterized fungal ABF-d3 enzymes aligned with GH43_36a, including the GH43_36 from Humicola insolens (HiABF43_36a). Hereto, the first fungal GH43_36b (from Talaromyces pinophilus) was cloned, purified, and characterized (TpABF43_36b). Surprisingly, TpABF43_36b was found to be active as ABF-m2,3, albeit with a relatively low rate compared to other ABFs tested, and showed minor xylanase activity. Novel specificities were also discovered for the HiABF43_36a, as it also released α-(1→2)-Ara from a disubstitution on the non-reducing end of an arabinoxylooligosaccharide (AXOS), and it was active to a lesser extent as an ABF-m2,3 towards AXOS when the Ara was on the second xylosyl from the non-reducing end. In essence, this work adds new insights into the biorefinery of agricultural residues.