This paper explores the realm of abstractive text summarization through the lens of the SEASON (Salience Allocation as Guidance for Abstractive SummarizatiON) technique, a model designed to enhance summarization by leveraging salience allocation techniques. The study evaluates SEASON's efficacy by comparing it with prominent models like BART, PEGASUS, and ProphetNet, all fine-tuned for various text summarization tasks. The assessment is conducted using diverse datasets including CNN/Dailymail, SAMSum, and Financial-news based Event-Driven Trading (EDT), with a specific focus on a financial dataset containing a substantial volume of news articles from 2020/03/01 to 2021/05/06. This paper employs various evaluation metrics such as ROUGE, METEOR, BERTScore, and MoverScore to evaluate the performance of these models fine-tuned for generating abstractive summaries. The analysis of these metrics offers a thorough insight into the strengths and weaknesses demonstrated by each model in summarizing news dataset, dialogue dataset and financial text dataset. The results presented in this paper not only contribute to the evaluation of the SEASON model's effectiveness but also illuminate the intricacies of salience allocation techniques across various types of datasets.

Sally the salad-making robot has arrived, and she may be the next big thing that can satisfy your customers' hunger for food-service automation. The creation of Redwood City, CA-based Chowbotics, Sally is a programmable robot that is about the size of dorm refrigerator. Using proprietary robotics technology, Sally can dispense and accurately measure 21 different healthy ingredients, including romaine, kale, seared chicken breast, Parmesan, California walnuts, cherry tomatoes and Kalamata olives. She mixes and dispenses the ingredients, while maintaining a precise temperature control. The foodie robot can craft 1,000 unique salads, all while the customer watches.

Creating reasonable AI for sidekicks in games has proven to be a difficult challenge synthetizing player modelling and cooperative planning, both being problems hard by themselves. In this paper, we experiment with designing around these problems: we propose a cooperative puzzle-platformer game that was designed to look similarly to the mainstream of the genre, but to allow for an easy implementation of a quality sidekick AI, letting us test player reactions to the AI. The game was designed so that it is easy for the AI to find optimal solutions while the problem is relatively hard for a human player. We gathered survey responses from players who played the game online (N=28). While the AI sidekick was reported as likeable and helpful, players still reported greater enjoyment of the game when they were allowed to control the sidekick themselves. These findings indicate that the AI itself is not the only obstacle to truly enjoyable gameplay with an AI sidekick.

Report 82-33 Welcome to the MRS TUTOR!!! This tutor is designed to introduce you to the syntax and basic database accessing functions of MRS. This document is a transcript of an interaction with the MRS tutor. Reprinted by permission of the author. Funding for this work was provided by ONR Contract N00014-81-K-0004. Representation languages provide a way to store and retrieve facts from a computer. Since English is a grammatically and textually ambiguous language, representation systems use a more formal language to describe the world. The way in which the words or symbols of a language are put together to form phrases and sentences is termed the "syntax" or "grammar" of the language.

Interactive narrative is a form of storytelling that adapts to actions performed by users who assume the roles of story characters. To date, interactive narratives are built by hand. In this paper, we introduce Scheherazade, an intelligent system that automatically creates an interactive narrative about any topic from crowdsourced narratives. Our system leverages the experience and creativity of humans by crowdsourcing a corpus of linear narrative examples. It then constructs an executable plot graph, which is a knowledge structure that defines the legal space of an interactive narrative, by learning the plot events, execution precedence, and event separations. We demonstrate the system can successfully construct an interactive narrative based on noisy human input.

This paper takes new look on language and knowledge modelling for corpus linguistics. Using ideas of Chaitin, a line of argument is made against language/knowledge separation in Natural Language Processing. A simplistic model, that generalises approaches to language and knowledge, is proposed. One of hypothetical consequences of this model is Strong AI.

Cognitive systems with human-level intelligence must dis­play a wide range of abilities, including reasoning about the beliefs of others, hypothetical and future situations, quanti­fiers, probabilities, and counterfactuals. While each of these deals in some way with reasoning about alternative states of reality, no single knowledge representation framework deals with them in a unified and scalable manner. As a conse­quence it is difficult to build cognitive systems for domains that require each of these abilities to be used together. To enable this integration we propose a representational framework based on synchronizing beliefs between worlds. Using this framework, each of these tasks can be reformu­lated into a reasoning problem involving worlds. This demonstrates that the notions of worlds and inheritance can bring significant parsimony and broad new abilities to knowledge representation.

Internal conflict, in which a character is torn by opposing motivations, is central to drama. Actors portray such conflict in part by mimicking involuntary behaviors that occur as a result of such conflicts. In this paper, we examine the role of timing – pauses and hesitation, in particular – in internal conflict. We argue that virtual actors can be made more expressive if we can emulate the underlying structures of inhibition and conflict detection believed to operate in the human system. We discuss work in progress on this problem that uses the Twig procedural animation system.