Explanation-Based Learning for Diagnosis

We present explanation-based learning (EBL) methods aimed at improving the performance of diagnosis systems integrating associational and model-based components. We consider multiple-fault model-based diagnosis (MBD) systems and describe two learning architectures. One, EBLIA, is a method for learning in advance. The other, EBL(p), is a method for learning while doing. EBLIA precompiles models into associations and relies only on the associations during diagnosis. EBL(p) performs compilation during diagnosis whenever reliance on previously learned associational rules results in unsatisfactory performance—as defined by a given performance threshold p. We present results of empirical studies comparing MBD without learning versus EBLIA and EBL(p). The main conclusions are as follows. EBLIA is superior when it is feasible, but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required.     

PEG based hyperbranched polymeric hollow nanospheres

The synthesis of a new PEG based hyperbranched copolymer of poly(ethylene glycol) methyl ether methacrylate-co-ethylene glycol dimethacrylate (PEGMEMA-co-EGDMA) was achieved via a one-step in situ deactivation enhanced atom transfer radical polymerization (DE-ATRP). Then, hollow PEG based nanospheres were fabricated from this polymer using a solvent evaporation method and post-stabilisation strategy. Furthermore, the analysis using a cellular metabolic activity assay proved that the copolymer did not affect cellular metabolism, indicating that this PEG based polymeric nanosphere has potential for use in drug delivery applications.     

LT Revisited: Explanation-Based Learning and the Logic of Principia Mathematica

This paper descibes an explanation-based learning (EBL) system based on a version of Newell, Shaw, and Simon's LOGIC-THEORIST (LT). Results of applying this system to propositional calculus problems from Principia Mathematica are compared with results of applying several other versions of the same performance element to these problems. The primary goal of this study is to characterize and analyze differences between non-learning, rote learning (LT's original learning method), and EBL. Another aim is to provide a characterization of the performance of a simple problem solver in the context of the Principia problems, in the hope that these problems can be used as a benchmark for testing improved learning methods, just as problems like chess and the eight puzzle have been used as benchmarks in research on search methods.     

Massively Multiplexed Submicron Particle Patterning in Acoustically Driven Oscillating Nanocavities

Nanoacoustic fields are a promising method for particle actuation at the nanoscale, though THz frequencies are typically required to create nanoscale wavelengths. In this work, the generation of robust nanoscale force gradients is demonstrated using MHz driving frequencies via acoustic‐structure interactions. A structured elastic layer at the interface between a microfluidic channel and a traveling surface acoustic wave (SAW) device results in submicron acoustic traps, each of which can trap individual submicron particles. The acoustically driven deformation of nanocavities gives rise to time‐averaged acoustic fields which direct suspended particles toward, and trap them within, the nanocavities. The use of SAWs permits massively multiplexed particle manipulation with deterministic patterning at the single‐particle level. In this work, 300 nm diameter particles are acoustically trapped in 500 nm diameter cavities using traveling SAWs with wavelengths in the range of 20–80 µm with one particle per cavity. On‐demand generation of nanoscale acoustic force gradients has wide applications in nanoparticle manipulation, including bioparticle enrichment and enhanced catalytic reactions for industrial applications.