Proceedings of the AAAI Symposium Series最新文献

{"title":"ASMR: Aggregated Semantic Matching Retrieval Unleashing Commonsense Ability of LLM through Open-Ended Question Answering","authors":"Pei-Ying Lin, Erick Chandra, Jane Yung-jen Hsu","doi":"10.1609/aaaiss.v3i1.31195","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31195","url":null,"abstract":"Commonsense reasoning refers to the ability to make inferences, draw conclusions, and understand the world based on general knowledge and commonsense. Whether Large Language Models (LLMs) have commonsense reasoning ability remains a topic of debate among researchers and experts. When confronted with multiple-choice commonsense reasoning tasks, humans typically rely on their prior knowledge and commonsense to formulate a preliminary answer in mind. Subsequently, they compare this preliminary answer to the provided choices, and select the most likely choice as the final answer. We introduce Aggregated Semantic Matching Retrieval (ASMR) as a solution for multiple-choice commonsense reasoning tasks. To mimic the process of humans solving commonsense reasoning tasks with multiple choices, we leverage the capabilities of LLMs to first generate the preliminary possible answers through open-ended question which aids in enhancing the process of retrieving relevant answers to the question from the given choices. Our experiments demonstrate the effectiveness of ASMR on popular commonsense reasoning benchmark datasets, including CSQA, SIQA, and ARC (Easy and Challenge). ASMR achieves state-of-the-art (SOTA) performance with a peak of +15.3% accuracy improvement over the previous SOTA on SIQA dataset.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"30 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Fast and Slow: A Redux of Levels of Learning in General Autonomous Intelligent Agents","authors":"Shiwali Mohan, John E. Laird","doi":"10.1609/aaaiss.v3i1.31279","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31279","url":null,"abstract":"Autonomous intelligent agents, including humans, operate in a complex, dynamic environment that necessitates continuous learning. We revisit our thesis that proposes that learning in human-like agents can be categorized into two levels: Level 1 (L1) involving innate and automatic learning mechanisms, while Level 2 (L2) comprises deliberate strategies controlled by the agent. Our thesis draws from our experiences in building artificial agents with complex learning behaviors, such as interactive task learning and open-world learning.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"31 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Responsible Integration of Large Language Models (LLMs) in Navy Operational Plan Generation","authors":"Simon Kapiamba, H. Fouad, Ira S. Moskowitz","doi":"10.1609/aaaiss.v3i1.31179","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31179","url":null,"abstract":"This paper outlines an approach for assessing and quantifying\u0000the risks associated with integrating Large Language Models\u0000(LLMs) in generating naval operational plans. It aims to explore\u0000the potential benefits and challenges of LLMs in this\u0000context and to suggest a methodology for a comprehensive\u0000risk assessment framework.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"18 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analogy as the Swiss Army Knife of Human-like Learning","authors":"Kenneth D. Forbus","doi":"10.1609/aaaiss.v3i1.31272","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31272","url":null,"abstract":"There is ample psychological evidence that analogy is ubiquitous in human learning, suggesting that computational models of analogy can play important roles in AI systems that learn in human-like ways. This talk will provide evidence for this, focusing mostly on recent advances in hierarchical analogical learning and working-memory analogical generalizations.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"73 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perception-Dominant Control Types for Human/Machine Systems","authors":"Ted Goranson","doi":"10.1609/aaaiss.v3i1.31177","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31177","url":null,"abstract":"We explore a novel approach to complex domain modelling by emphasising primitives based on perception. The usual approach either focuses on actors or cognition associated with tokens that convey information. In related research, we have examined using effects and/or outcomes as primitives, and influences as the generator of those outcomes via categoric functors. \u0000 That approach (influences, effects) has advantages: it leverages what is known and supports the expanded logics we use, where we want to anticipate and engineer possible futures. But it has weaknesses when placed in a dynamic human-machine system where what is perceived or assumed matters more than what is known. The work reported here builds on previous advances in type specification and reasoning to ‘move the primitives forward’ more toward situation encounter and away from situation understanding. \u0000 The goal is in the context of shared human-machine systems where:\u0000• reaction times are shorter than the traditional ingestion/comprehension/response loop can support;\u0000• situations that are too complex or dynamic for current comprehension by any means;\u0000• there simply is insufficient knowledge about governing situations for the comprehension model to support action; and/or,\u0000• the many machine/human and system/system interfaces that are incapable of conveying the needed insights; that is, the communication channels choke the information or influence flows.\u0000 While the approach is motivated by the above unfriendly conditions, we expect significant benefits. We will explore these but engineer toward a federated decision paradigm where decisions by local human, machine or synthesis are not whole-situation-aware, but that collectively ‘swarm’ locally across the larger system to be more effective, ‘wiser’ than a convention paradigm may produce.\u0000 The supposed implementation strategy will be through extending an existing ‘playbooks as code’ project whose goals are to advise on local action by modelling and gaming complex system dynamics. A sponsoring context is ‘grey zone’ competition that avoids armed conflict, but that can segue to a mixed system course of action advisory. The general context is a costly ‘blue swan’ risk in large commercial and government enterprises.\u0000 The method will focus on patterns and relationships in synthetic categories used to model type transitions within topological models of system influence. One may say this is applied intuitionistic type theory, following mechanisms generally described by synthetic differential geometry. In this context, the motivating supposition of this study is that information-carrying influence channels are best modelled in our challenging domain as perceived types rather than understood types.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"100 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethical Considerations of Generative AI: A Survey Exploring the Role of Decision Makers in the Loop","authors":"Yohn Jairo Parra Bautista, Carlos Theran, Richard A. Aló","doi":"10.1609/aaaiss.v3i1.31243","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31243","url":null,"abstract":"We explore the foresighted concerns that Norbert Wiener voiced in 1960 about the potential of machines to learn and create strategies that could not be anticipated, drawing parallels to the fable \"The Sorcerer's Apprentice\" by Goethe. The progress in artificial intelligence (AI) has brought these worries back to the forefront, as shown by a survey AI Impacts conducted in 2022 with more than 700 machine learning researchers. This survey found a five percentage probability that advanced AI might cause \"extremely adverse\" outcomes, including the possibility of human extinction. Importantly, the introduction of OpenAI's ChatGPT, powered by GPT-4, has led to a surge in entrepreneurial activities, highlighting the ease of use of large language models (LLMs).AI's potential for adverse outcomes, such as military control and unregulated AI races, is explored alongside concerns about AI's role in governance, healthcare, media portrayal, and surpassing human intelligence. Given their transformative impact on content creation, the prominence of generative AI tools such as ChatGPT is noted. The societal assessment of Artificial Intelligence (AI) has grown increasingly intricate and pressing in tandem with the rapid evolution of this technology. As AI continues to advance at a swift pace, the need to comprehensively evaluate its societal implications has become more complex and urgent, necessitating a thorough examination of its potential impact on various domains such as governance, healthcare, media portrayal, and surpassing human intelligence. This assessment is crucial in addressing ethical concerns related to bias, data misuse, technical limitations, and transparency gaps, and in integrating ethical and legal principles throughout AI algorithm lifecycles to ensure alignment with societal well-being. Furthermore, the urgency of addressing the societal implications of AI is underscored by the need for healthcare workforce upskilling and ethical considerations in the era of AI-assisted medicine, emphasizing the critical importance of integrating societal well-being into the development and deployment of AI technologies. Our study entails an examination of the ethical quandaries and obstacles presented when developing methods to evaluate and predict the broader societal impacts of AI on decision-making processes involving the generating of images, videos, and textual content.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"85 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework for Federated Learning and Edge Deployment of Real-Time Reinforcement Learning Decision Engine on Software Defined Radio","authors":"Jithin Jagannath","doi":"10.1609/aaaiss.v3i1.31218","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31218","url":null,"abstract":"Machine learning promises to empower dynamic resource allocation requirements of Next Generation (NextG) wireless networks including 6G and tactical networks. Recently, we have seen the impact machine learning can make on various aspects of wireless networks. Yet, in most cases, the progress has been limited to simulations and/or relies on large processing units to run the decision engines as opposed to deploying it on the radio at the edge. While relying on simulations for rapid and efficient training of deep reinforcement learning (DRL) may be necessary, it is key to mitigate the sim-real gap while trying to improve the generalization capability. To mitigate these challenges, we developed the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet Gym), an open-source architecture designed for accelerating the deployment of novel DRL for NextG wireless networks. To demonstrate its impact, we tackled the problem of distributed frequency and power allocation while emphasizing the generalization capability of DRL decision engine. The end-to-end solution was implemented on the GPU-embedded software-defined radio and validated using over-the-air evaluation. To the best of our knowledge, these were the first instances that established the feasibility of deploying DRL for optimized distributed resource allocation for next-generation of GPU-embedded radios.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"76 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can LLMs Answer Investment Banking Questions? Using Domain-Tuned Functions to Improve LLM Performance on Knowledge-Intensive Analytical Tasks","authors":"Nicholas Harvel, F. B. Haiek, Anupriya Ankolekar, David James Brunner","doi":"10.1609/aaaiss.v3i1.31191","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31191","url":null,"abstract":"Large Language Models (LLMs) can increase the productivity of general-purpose knowledge work, but accuracy is a concern, especially in professional settings requiring domain-specific knowledge and reasoning. To evaluate the suitability of LLMs for such work, we developed a benchmark of 16 analytical tasks representative of the investment banking industry. We evaluated LLM performance without special prompting, with relevant information provided in the prompt, and as part of a system giving the LLM access to domain-tuned functions for information retrieval and planning. Without access to functions, state-of-the-art LLMs performed poorly, completing two or fewer tasks correctly. Access to appropriate domain-tuned functions yielded dramatically better results, although performance was highly sensitive to the design of the functions and the structure of the information they returned. The most effective designs yielded correct answers on 12 out of 16 tasks. Our results suggest that domain-specific functions and information structures, by empowering LLMs with relevant domain knowledge and enabling them to reason in domain-appropriate ways, may be a powerful means of adapting LLMs for use in demanding professional settings.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"96 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploiting Machine Learning Bias: Predicting Medical Denials","authors":"Stephen Russell, Fabio Montes Suros, Ashwin Kumar","doi":"10.1609/aaaiss.v3i1.31181","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31181","url":null,"abstract":"For a large healthcare system, ignoring costs associated with managing the patient encounter denial process (staffing, contracts, etc.), total denial-related amounts can be more than $1B annually in gross charges. Being able to predict a denial before it occurs has the potential for tremendous savings. Using machine learning to predict denial has the potential to allow denial-preventing interventions. However, challenges of data imbalance make creating a single generalized model difficult. We employ two biased models in a hybrid voting scheme to achieve results that exceed the state-of-the art and allow for incremental predictions as the encounter progresses. The model had the added benefit of monitoring the human-driven denial process that affect the underlying distribution, on which the models’ bias is based.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"60 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI for Social Good Education at Hispanic Serving Institutions","authors":"Yu Chen, Gabriel Granco, Yunfei Hou, Heather Macias, Frank A. Gomez","doi":"10.1609/aaaiss.v3i1.31259","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31259","url":null,"abstract":"This project aims to broaden AI education by developing and studying the efficacy of innovative learning practices and resources for AI education for social good. We have developed three AI learning modules for students to: 1) identify social issues that align with the SDGs in their community (e.g., poverty, hunger, quality education); 2) learn AI through hands-on labs and business applications; and 3) create AI-powered solutions in teams to address social is-sues they have identified. Student teams are expected to situate AI learning in their communities and contribute to their communities. Students then use the modules to en-gage in an interdisciplinary approach, facilitating AI learn-ing for social good in informational sciences and technology, geography, and computer science at three CSU HSIs (San Jose State University, Cal Poly Pomona and CSU San Bernardino). Finally, we aim to evaluate the efficacy and impact of the proposed AI teaching methods and activities in terms of learning outcomes, student experience, student engagement, and equity.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"28 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}