Script Combination for Enhanced Story Understanding and Story Generation Systems

Scripts, knowledge structures defining sequences of events in stereotypical social situations, were traditionally used to simulate the ways in which people can infer unstated details in understanding a story. In this paper, we describe the MUltiple SCRipt AcTivator (MUSCRAT), and the Script Combination Applier Mechanism (SCAM), significant enhancements of Cullingford’s Script Applier Mechanism which accomplish two novel aims. One system, MUSCRAT, is able to activate more than one script and use them during a story understanding process. The second system, SCAM, uses scripts for story generation, using script variables as “terminals” for combining two or more scripts together to create complex narrative situation structures. The combinational abilities of these systems are enhanced by the fact that the scripts are represented not using linguistic elements but using decompositions into abstract conceptual primitives. We also discuss how some of the perceived weaknesses of scripts stemming from prior work may be overcome in systems that represent general thinking and reasoning processes as combined instantiations of standardized and generalized memory episodes

Generative AI for Product Design: Getting the Right Design and the Design Right

Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to professional practice across industries, including product design. While these generative capabilities may seem enticing on the surface, certain barriers limit their practical application for real-world use in industry settings. In this position paper, we articulate and situate these barriers within two phases of the product design process, namely "getting the right design" and "getting the design right," and propose a research agenda to stimulate discussions around opportunities for realizing the full potential of GenAI tools in product design

Interleaving a Symbolic Story Generator with a Neural Network-Based Large Language Model

Research in deep learning has recently produced models of natural language that are capable of generating natural language output which, at a glance, has strong similarities to that written by intelligent humans. However, the texts produced by deep learning-based large language models (LLMs), upon deeper examination, reveal the challenges that they have in producing outputs that maintain logical coherence. One specific application area of interest for LLMs is in fictional narrative generation, a mode of operation in which stories are generated by the model in response to a prompt text that indicates the start of a story or the desired style of writing to be produced. In this paper we present an initial study into a method for combining an LLM with a symbolic system to perform story generation. The method generates stories by repeatedly prompting the LLM by interleaving the output of a state-of-the-art LLM with the output of a classic story generation system, while attempting to control and shape the output of the LLM. We present a number of stories generated with a prototype interleaving system, and discuss the qualities of the stories and challenges for future development of the method

More Human than Human: LLM-Generated Narratives Outperform Human-LLM Interleaved Narratives

Narrative story generation has gained emerging interest in the field of large language models. The present paper aims to compare stories generated by an LLM only (non-interleaved) with those generated by interleaving human-generated and LLM-generated text (interleaved). The study’s hypothesis is that interleaved stories would perform better than non-interleaved stories. To verify this hypothesis, we conducted two tests with roughly 500 participants each. Participants were asked to rate stories of each type, including an overall score or preference and four facets—logical soundness, plausibility, understandability, and novelty. Our findings indicate that interleaved stories were in fact less preferred than non-interleaved stories. The result has implications for the design and implementation of our story generators. This study contributes new insights into the potential uses and restrictions of interleaved and non-interleaved systems regarding generating narrative stories, which may help to improve the performance of such story generators

Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies.

Metagenomic sequence data from defined mock communities is crucial for the assessment of sequencing platform performance and downstream analyses, including assembly, binning and taxonomic assignment. We report a comparison of shotgun metagenome sequencing and assembly metrics of a defined microbial mock community using the Oxford Nanopore Technologies (ONT) MinION, PacBio and Illumina sequencing platforms. Our synthetic microbial community BMock12 consists of 12 bacterial strains with genome sizes spanning 3.2-7.2 Mbp, 40-73% GC content, and 1.5-7.3% repeats. Size selection of both PacBio and ONT sequencing libraries prior to sequencing was essential to yield comparable relative abundances of organisms among all sequencing technologies. While the Illumina-based metagenome assembly yielded good coverage with few misassemblies, contiguity was greatly improved by both, Illumina + ONT and Illumina + PacBio hybrid assemblies but increased misassemblies, most notably in genomes with high sequence similarity to each other. Our resulting datasets allow evaluation and benchmarking of bioinformatics software on Illumina, PacBio and ONT platforms in parallel

High quality draft genome sequence of Bacteroides barnesiae type strain BL2T (DSM 18169T) from chicken caecum

Bacteroides barnesiae Lan et al. 2006 is a species of the genus Bacteroides, which belongs to the family Bacteroidaceae. Strain BL2(T) is of interest because it was isolated from the gut of a chicken and the growing awareness that the anaerobic microbiota of the caecum is of benefit for the host and may impact poultry farming. The 3,621,509 bp long genome with its 3,059 protein-coding and 97 RNA genes is a part of the Genomic Encyclopedia of Type Strains, Phase I: the one thousand microbial genomes (KMG) project

High quality draft genome sequence of the slightly halophilic bacterium Halomonas zhanjiangensis type strain JSM 078169T (DSM 21076T) from a sea urchin in southern China

Halomonas zhanjiangensis Chen et al. 2009 is a member of the genus Halomonas, family Halomonadaceae, class Gammaproteobacteria. Representatives of the genus Halomonas are a group of halophilic bacteria often isolated from salty environments. The type strain H. zhanjiangensis JSM 078169(T) was isolated from a sea urchin (Hemicentrotus pulcherrimus) collected from the South China Sea. The genome of strain JSM 078169(T) is the fourteenth sequenced genome in the genus Halomonas and the fifteenth in the family Halomonadaceae. The other thirteen genomes from the genus Halomonas are H. halocynthiae, H. venusta, H. alkaliphila, H. lutea, H. anticariensis, H. jeotgali, H. titanicae, H. desiderata, H. smyrnensis, H. salifodinae, H. boliviensis, H. elongata and H stevensii. Here, we describe the features of strain JSM 078169(T), together with the complete genome sequence and annotation from a culture of DSM 21076(T). The 4,060,520 bp long draft genome consists of 17 scaffolds with the 3,659 protein-coding and 80 RNA genes and is a part of Genomic Encyclopedia of Type Strains, Phase I: the one thousand microbial genomes (KMG) project

Draft genome sequence of Halomonas lutea strain YIM 91125T (DSM 23508T) isolated from the alkaline Lake Ebinur in Northwest China

Species of the genus Halomonas are halophilic and their flexible adaption to changes of salinity and temperature brings considerable potential biotechnology applications, such as degradation of organic pollutants and enzyme production. The type strain Halomonas lutea YIM 91125(T) was isolated from a hypersaline lake in China. The genome of strain YIM 91125(T) becomes the twelfth species sequenced in Halomonas, and the thirteenth species sequenced in Halomonadaceae. We described the features of H. lutea YIM 91125(T), together with the high quality draft genome sequence and annotation of its type strain. The 4,533,090 bp long genome of strain YIM 91125(T) with its 4,284 protein-coding and 84 RNA genes is a part of Genomic Encyclopedia of Type Strains, Phase I: the one thousand microbial genomes (KMG-I) project. From the viewpoint of comparative genomics, H. lutea has a larger genome size and more specific genes, which indicated acquisition of function bringing better adaption to its environment. DDH analysis demonstrated that H. lutea is a distinctive species, and halophilic features and nitrogen metabolism related genes were discovered in its genome