Ensemble-Based Deep Reinforcement Learning for Chatbots

Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this challenge, but its successful application remains an open question. This article describes a novel ensemble-based approach applied to value-based DRL chatbots, which use finite action sets as a form of meaning representation. In our approach, while dialogue actions are derived from sentence clustering, the training datasets in our ensemble are derived from dialogue clustering. The latter aim to induce specialised agents that learn to interact in a particular style. In order to facilitate neural chatbot training using our proposed approach, we assume dialogue data in raw text only – without any manually-labelled data. Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent. In addition to evaluations using held-out data, our results are further supported by a human evaluation that rated dialogues in terms of fluency, engagingness and consistency – which revealed that our proposed dialogue rewards strongly correlate with human judgements

Investigation of Stability and Power Consumption of an AlGaN/GaN Heterostructure Hydrogen Gas Sensor Using Different Bias Conditions

A Pd-functionalized hydrogen gas sensor was fabricated on an AlGaN/GaN-on-Si heterostructure platform. The AlGaN layer under the Pd catalyst area was partially recessed by plasma etching, which resulted in a low standby current level enhancing the sensor response. Sensor stability and power consumption depending on operation conditions were carefully investigated using two different bias modes: constant voltage bias mode and constant current bias mode. From the stability point of view, high voltage operation is better than low voltage operation for the constant voltage mode of operation, whereas low current operation is preferred over high current operation for the constant current mode of operation. That is, stable operation with lower standby power consumption can be achieved with the constant current bias operation. The fabricated AlGaN/GaN-on-Si hydrogen sensor exhibited excellent sensing characteristics; a response of 120% with a response time of < 0.4 s at a bias current density of 1 mA/mm at 200 °C. The standby power consumption was only 0.54 W/cm2 for a sensing catalyst area of 100 × 24 μm2

Impact of Molecular Weight on Molecular Doping Efficiency of Conjugated Polymers and Resulting Thermoelectric Performances

The effect of molecular weight of a series of conjugated polymers (CPs) on the doping efficiency, electrical conductivity, and related thermoelectric properties of doped CPs is studied. Low (L), medium (M), and high (H) molecular weight batches of PDFD-T polymers, based on difluorobenzothia-diazole and dithienosilole moieties, are synthesized and denoted as PDFD-T(L), PDFD-T(M), and PDFD-T(H), respectively. Furthermore, to compare the effects of different donor moieties, donor units of PDFD-T(L) are structurally modified from thiophene to thienothiophene (TT) and dithienothiophene (DTT), denoted as PDFD-TT(L) and PDFD-DTT(L), respectively. After doping the CPs with FeCl3, d-PDFD-T(H) exhibits an electrical conductivity of 402.9 S cm(-1), which is significantly higher than those of d-PDFD-T(L), d-PDFD-T(M), d-PDFD-TT(L), and d-PDFD-DTT(L). The highest power factor of 101.1 mu W m(-1) K-2 is achieved through organic thermoelectric devices fabricated using PDFD-T(H). Through various characterizations, it is demonstrated that CPs with a high molecular weight tend to have a high carrier mobility while maintaining their original crystallinity and good charge trans- port pathways even after doping. Therefore, it is suggested that optimizing the molecular weight of CPs is an essential strategy for maximal power generation from their doped CP films.11Nsciescopu

Photochemical Hydrogen Doping Induced Embedded Two-Dimensional Metallic Channel Formation in InGaZnO at Room Temperature

The photochemical tunability of the charge-transport mechanism in metal-oxide semiconductors is of great interest since it may offer a facile but effective semiconductor-to-metal transition, which results from photochemically modified electronic structures for various oxide-based device applications. This might provide a feasible hydrogen (H)-radical doping to realize the effectively H-doped metal oxides, which has not been achieved by thermal and ion-implantation technique in a reliable and controllable way. In this study, we report a photochemical conversion of InGaZnO (IGZO) semiconductor to a transparent conductor via hydrogen doping to the local nanocrystallites formed at the IGZO/glass interface at room temperature. In contrast to thermal or ionic hydrogen doping, ultraviolet exposure of the IGZO surface promotes a photochemical reaction with H radical incorporation to surface metal–OH layer formation and bulk H-doping which acts as a tunable and stable highly doped n-type doping channel and turns IGZO to a transparent conductor. This results in the total conversion of carrier conduction property to the level of metallic conduction with sheet resistance of ∼16 Ω/□, room temperature Hall mobility of 11.8 cm² V^–1 sec^–1, the carrier concentration at ∼10²⁰ cm^–3 without any loss of optical transparency. We demonstrated successful applications of photochemically highly n-doped metal oxide via optical dose control to transparent conductor with excellent chemical and optical doping stability