GenAI4ELab/papercli-papers huggingface dataset:
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
Authors: Yixiao Zhang; Yukara Ikemiya; Gus Xia; Naoki Murata; Marco A. Martínez-Ramírez; Wei-Hsiang Liao; Yuki Mitsufuji; Simon Dixon
Venue: IJCAI 2024 (main)

Abstract: Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, the task of editing these generated music remains a significant challenge. This paper introduces a novel approach to edit music generated by such models, enabling the modification of specific attributes, such as genre, mood, and instrument, while maintaining other aspects unchanged. Our method transforms text editing to the latent space manipulation, and adds an additional constraint to enforce consistency. It seamlessly integrates with existing pretrained text-to-music diffusion models without requiring additional training. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations. We also show the practical applicability of our approach in real-world music editing scenarios.
Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt
Authors: Zhanjie Zhang; Quanwei Zhang; Huaizhong Lin; Wei Xing; Juncheng Mo; Shuaicheng Huang; Jinheng Xie; Guangyuan Li; Junsheng Luan; Lei Zhao; Dalong Zhang; Lixia Chen
Venue: IJCAI 2024 (main)

Abstract: Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always introduce obvious artifacts and disharmonious patterns. Recently, large-scale pre-trained diffusion models opened up a new way for generating highly realistic artistic stylized images. However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. Specifically, we introduce a Step-aware and Layer-aware Prompt Space, a set of learnable prompts, which can learn the style information from the collection of artworks and dynamically adjusts the input images' content structure and style pattern. To train our prompt space, we propose a novel inversion method, called Step-ware and Layer-aware Prompt Inversion, which allows the prompt space to learn the style information of the artworks collection. In addition, we inject a pre-trained conditional branch of ControlNet into our LSAST, which further improved our framework's ability to maintain content structure. Extensive experiments demonstrate that our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods. Code is available at https://github.com/Jamie-Cheung/LSAST.
Are They the Same Picture? Adapting Concept Bottleneck Models for Human-AI Collaboration in Image Retrieval
Authors: Vaibhav Balloli; Sara Beery; Elizabeth Bondi-Kelly
Venue: IJCAI 2024 (main)

Abstract: Image retrieval plays a pivotal role in applications from wildlife conservation to healthcare, for finding individual animals or relevant images to aid diagnosis. Although deep learning techniques for image retrieval have advanced significantly, their imperfect real-world performance often necessitates including human expertise. Human-in-the-loop approaches typically rely on humans completing the task independently and then combining their opinions with an AI model in various ways, as these models offer very little interpretability or correctability. To allow humans to intervene in the AI model instead, thereby saving human time and effort, we adapt the Concept Bottleneck Model (CBM) and propose CHAIR. CHAIR (a) enables humans to correct intermediate concepts, which helps \textit{improve} embeddings generated, and (b) allows for flexible levels of intervention that accommodate varying levels of human expertise for better retrieval. To show the efficacy of \methodname, we demonstrate that our method performs better than similar models on image retrieval metrics without any external intervention. Furthermore, we also showcase how human intervention helps further improve retrieval performance, thereby achieving human-AI complementarity.
Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication
Authors: Shenghui Chen; Daniel Fried; Ufuk Topcu
Venue: IJCAI 2024 (main)

Abstract: Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent in this game with a human as the other player. To solve this problem, we propose a communication-based approach comprising a language module and a planning module. The language module translates natural language messages into and from a finite set of flags, a compact representation defined to capture player intents. The planning module leverages these flags to compute a policy using an asymmetric information-set Monte Carlo tree search with flag exchange algorithm we present. We evaluate the effectiveness of this approach in a testbed based on Gnomes at Night, a search-and-find maze board game. Results of human subject experiments show that communication narrows the information gap between players and enhances human-agent cooperation efficiency with fewer turns.
Data Ownership and Privacy in Personalized AI Models in Assistive Healthcare
Authors: Boris Debic; Luka Medvidovic
Venue: IJCAI 2024 (main)

Abstract: The use of personalized artificial intelligence (AI) models in assistive healthcare presents a number of ethical and legal challenges, they are examined in this paper. We look at a number of situations in which AI models have close interactions with persons under care. In particular we are interested in the question of model and data ownership, privacy, and ethical implications. The paper also surveys the existing regulatory environment, including the US Congress's legislative initiatives and the US Federal Trade Commission's examination of AI. Additionally, it covers AI strategies such as modular AI and discusses possible solutions to the issues described. We present an overview of the current outstanding problems and with this work offer a researched and organized contribution for a public discussion of responsible application of AI in this field of healthcare. Our selection of topics is guided by keeping in mind the key stakeholders: technology providers, healthcare or care providers, care beneficiaries and their families.
Towards Proactive Interactions for In-Vehicle Conversational Assistants Utilizing Large Language Models
Authors: Huifang Du; Xuejing Feng; Jun Ma; Meng Wang; Shiyu Tao; Yijie Zhong; Yuan-Fang Li; Haofen Wang
Venue: IJCAI 2024 (main)

Abstract: Research demonstrates that the proactivity of in-vehicle conversational assistants (IVCAs) can help to reduce distractions and enhance driving safety, better meeting users' cognitive needs. However, existing IVCAs struggle with user intent recognition and context awareness, which leads to suboptimal proactive interactions. Large language models (LLMs) have shown potential for generalizing to various tasks with prompts, but their application in IVCAs and exploration of proactive interaction remain under-explored. These raise questions about how LLMs improve proactive interactions for IVCAs and influence user perception. To investigate these questions systematically, we establish a framework with five proactivity levels across two dimensions—assumption and autonomy—for IVCAs. According to the framework, we propose a ``Rewrite + ReAct + Reflect'' strategy, aiming to empower LLMs to fulfill the specific demands of each proactivity level when interacting with users. Both feasibility and subjective experiments are conducted. The LLM outperforms the state-of-the-art model in success rate and achieves satisfactory results for each proactivity level. Subjective experiments with 40 participants validate the effectiveness of our framework and show the proactive level with strong assumptions and user confirmation is most appropriate.
A Goal-Directed Dialogue System for Assistance in Safety-Critical Application
Authors: Prakash Jamakatel; Rebecca De Venezia; Christian Muise; Jane Jean Kiam
Venue: IJCAI 2024 (main)

Abstract: In safety-critical applications where a human is in the loop, providing timely contextual assistance can reduce the severity of emergencies. While the context can typically be inferred passively, engaging the human in an active conversation with the assistance system makes this context richer and more sound. For this, we explore a FOND-planning-powered goal-directed dialogue system with Natural Language Understanding (NLU) capabilities. We use an Ultralight (UL) aviation domain as an example application for test and validation by inferring the current context in situations requiring emergency landings using the goal-directed dialogue system. The inferred context is then used for real-time modelling of the problem instance, necessary for generating strategic plans to guide the human out of the emergency situations. To overcome data scarcity, we augment the data collected from human pilots using generative text models to train the NLU capabilities of the dialogue agent. We benchmark against generative chatbots and demonstrate that our goal-directed dialogue system significantly outperforms them in context inference.
The Role of Perception, Acceptance, and Cognition in the Usefulness of Robot Explanations
Authors: Hana Kopecka; Jose Such; Michael Luck
Venue: IJCAI 2024 (main)

Abstract: It is known that when interacting with explainable autonomous systems, user characteristics are important in determining the most appropriate explanation, but understanding which user characteristics are most relevant to consider is not simple. This paper explores such characteristics and analyses how they affect the perceived usefulness of four types of explanations based on the robot's mental states. These types are belief, goal, hybrid (goal and belief) and baseline explanations. In this study, the explanations were evaluated in the context of a domestic service robot. The user characteristics considered are the perception of the robot's rationality and autonomy, the acceptance of the robot and the user's cognitive tendencies. We found differences in perceived usefulness between explanation types based on user characteristics, with hybrid explanations being the most useful.
XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations
Authors: Qihao Liang; Xichu Ma; Finale Doshi-Velez; Brian Lim; Ye Wang
Venue: IJCAI 2024 (main)

Abstract: Explaining the singability of lyrics is an important but missing ability of language models (LMs) in song lyrics generation. This ability allows songwriters to quickly assess if LM-generated lyrics can be sung harmoniously with melodies and helps singers align lyrics with melodies during practice. This paper presents XAI-Lyricist, leveraging musical prosody to guide LMs in generating singable lyrics and providing human-understandable singability explanations. We employ a Transformer model to generate lyrics under musical prosody constraints and provide demonstrations of the lyrics' prosody patterns as singability explanations. XAI-Lyricist is evaluated by computational metrics (perplexity, prosody-BLEU) and a human-grounded study (human ratings, average time and number of attempts for singing). Experimental results show that musical prosody can significantly improve the singability of LM-generated lyrics. A controlled study with 14 singers also confirms the usefulness of the provided explanations in helping them to interpret lyrical singability faster than reading plain text lyrics.
From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News
Authors: Yuhan Liu; Xiuying Chen; Xiaoqing Zhang; Xing Gao; Ji Zhang; Rui Yan
Venue: IJCAI 2024 (main)

Abstract: In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world complexities and overlook the rich semantic information of news text. The advent of large language models (LLMs) provides the possibility of modeling subtle dynamics of opinion. Consequently, in this work, we introduce a Fake news Propagation Simulation framework (FPS) based on LLM, which studies the trends and control of fake news propagation in detail. Specifically, each agent in the simulation represents an individual with a distinct personality. They are equipped with both short-term and long-term memory, as well as a reflective mechanism to mimic human-like thinking. Every day, they engage in random opinion exchanges, reflect on their thinking, and update their opinions. Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations. Additionally, we evaluate various intervention strategies and demonstrate that early and appropriately frequent interventions strike a balance between governance cost and effectiveness, offering valuable insights for practical applications. Our study underscores the significant utility and potential of LLMs in combating fake news.
Emergence of Social Norms in Generative Agent Societies: Principles and Architecture
Authors: Siyue Ren; Zhiyao Cui; Ruiqi Song; Zhen Wang; Shuyue Hu
Venue: IJCAI 2024 (main)

Abstract: Social norms play a crucial role in guiding agents towards understanding and adhering to standards of behavior, thus reducing social conflicts within multi-agent systems (MASs). However, current LLM-based (or generative) MASs lack the capability to be normative. In this paper, we propose a novel architecture, named CRSEC, to empower the emergence of social norms within generative MASs. Our architecture consists of four modules: Creation & Representation, Spreading, Evaluation, and Compliance. This addresses several important aspects of the emergent processes all in one: (i) where social norms come from, (ii) how they are formally represented, (iii) how they spread through agents' communications and observations, (iv) how they are examined with a sanity check and synthesized in the long term, and (v) how they are incorporated into agents' planning and actions. Our experiments deployed in the Smallville sandbox game environment demonstrate the capability of our architecture to establish social norms and reduce social conflicts within generative MASs. The positive outcomes of our human evaluation, conducted with 30 evaluators, further affirm the effectiveness of our approach. Our project can be accessed via the following link: https://github.com/sxswz213/CRSEC.
ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments
Authors: Sören Schleibaum; Lu Feng; Sarit Kraus; Jörg P. Müller
Venue: IJCAI 2024 (main)

Abstract: In the evolving landscape of human-centered AI, fostering a synergistic relationship between humans and AI agents in decision-making processes stands as a paramount challenge. This work considers a problem setup where an intelligent agent comprising a neural network-based prediction component and a deep reinforcement learning component provides advice to a human decision-maker in complex repeated decision-making environments. Whether the human decision-maker would follow the agent's advice depends on their beliefs and trust in the agent and on their understanding of the advice itself. To this end, we developed an approach named ADESSE to generate explanations about the adviser agent to improve human trust and decision-making. Computational experiments on a range of environments with varying model sizes demonstrate the applicability and scalability of ADESSE. Furthermore, an interactive game-based user study shows that participants were significantly more satisfied, achieved a higher reward in the game, and took less time to select an action when presented with explanations generated by ADESSE. These findings illuminate the critical role of tailored, human-centered explanations in AI-assisted decision-making.
Reassessing Evaluation Functions in Algorithmic Recourse: An Empirical Study from a Human-Centered Perspective
Authors: Tomu Tominaga; Naomi Yamashita; Takeshi Kurashima
Venue: IJCAI 2024 (main)

Abstract: In this study, we critically examine the foundational premise of algorithmic recourse - a process of generating counterfactual action plans (i.e., recourses) assisting individuals to reverse adverse decisions made by AI systems. The assumption underlying algorithmic recourse is that individuals accept and act on recourses that minimize the gap between their current and desired states. This assumption, however, remains empirically unverified. To address this issue, we conducted a user study with 362 participants and assessed whether minimizing the distance function, a metric of the gap between the current and desired states, indeed prompts them to accept and act upon suggested recourses. Our findings reveal a nuanced landscape: participants' acceptance of recourses did not correlate with the recourse distance. Moreover, participants' willingness to act upon recourses peaked at the minimal recourse distance but was otherwise constant. These findings cast doubt on the prevailing assumption of algorithmic recourse research and signal the need to rethink the evaluation functions to pave the way for human-centered recourse generation.
A Survey on Model-Free Goal Recognition
Authors: Leonardo Amado; Sveta Paster Shainkopf; Ramon Fraga Pereira; Reuth Mirsky; Felipe Meneguzzi
Venue: IJCAI 2024 (main)

Abstract: Goal Recognition is the task of inferring an agent's intentions from a set of observations. Existing recognition approaches have made considerable advances in domains such as human-robot interaction, intelligent tutoring systems, and surveillance. However, most approaches rely on explicit domain knowledge, often defined by a domain expert. Much recent research focus on mitigating the need for a domain expert while maintaining the ability to perform quality recognition, leading researchers to explore Model-Free Goal Recognition approaches. We comprehensively survey Model-Free Goal Recognition, and provide a perspective on the state-of-the-art approaches and their applications, showing recent advances. We categorize different approaches, introducing a taxonomy with a focus on their characteristics, strengths, weaknesses, and suitability for different scenarios. We compare the advances each approach made to the state-of-the-art and provide a direction for future research in Model-Free Goal Recognition.
Medical Neural Architecture Search: Survey and Taxonomy
Authors: Hadjer Benmeziane; Imane Hamzaoui; Zayneb Cherif; Kaoutar El Maghraoui
Venue: IJCAI 2024 (main)

Abstract: This paper presents a comprehensive survey of Medical Neural Architecture Search (MedNAS), a burgeoning field at the confluence of deep learning and medical imaging. With the increasing prevalence of FDA-approved medical deep learning models, MedNAS emerges as a key area in leveraging computational innovations for healthcare advancements. Our survey examines the paradigm shift introduced by Neural Architecture Search (NAS), which automates neural network design, replacing traditional, manual designs. We explore the unique search spaces tailored for medical tasks on different types of data from images to EEG, the methodologies of MedNAS, and their impact on medical applications.
A Survey on Plan Optimization
Authors: Pascal Bercher; Patrik Haslum; Christian Muise
Venue: IJCAI 2024 (main)

Abstract: Automated Planning deals with finding a sequence of actions that solves a given (planning) problem. The cost of the solution is a direct consequence of these actions, for example its number or their accumulated costs. Thus, in most applications, cheaper plans are preferred. Yet, finding an optimal solution is more challenging than finding some solution. So, many planning algorithms find some solution and then post-process, i.e., optimize it -- a technique called plan optimization. Over the years many different approaches were developed, not all for the same kind of plans, and not all optimize the same metric. In this comprehensive survey, we give an overview of the existing plan optimization goals, their computational complexity (if known), and existing techniques for such optimizations.
Policy Space Response Oracles: A Survey
Authors: Ariyan Bighashdel; Yongzhao Wang; Stephen McAleer; Rahul Savani; Frans A. Oliehoek
Venue: IJCAI 2024 (main)

Abstract: Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the large number of strategies, precluding direct application to more complex scenarios. This survey provides a comprehensive overview of a framework for large games, known as Policy Space Response Oracles (PSRO), which holds promise to improve scalability by focusing attention on sufficient subsets of strategies. We first motivate PSRO and provide historical context. We then focus on the strategy exploration problem for PSRO: the challenge of assembling effective subsets of strategies that still represent the original game well with minimum computational cost. We survey current research directions for enhancing the efficiency of PSRO, and explore the applications of PSRO across various domains. We conclude by discussing open questions and future research.
Guide to Numerical Experiments on Elections in Computational Social Choice
Authors: Niclas Boehmer; Piotr Faliszewski; Łukasz Janeczko; Andrzej Kaczmarczyk; Grzegorz Lisowski; Grzegorz Pierczyński; Simon Rey; Dariusz Stolicki; Stanisław Szufa; Tomasz Wąs
Venue: IJCAI 2024 (main)

Abstract: We analyze how numerical experiments regarding elections were conducted within computational social choice literature (focusing on papers published in the IJCAI, AAAI, and AAMAS conferences). We analyze the sizes of the studied elections and the methods of generating preference data, thereby making previously hidden standards and practices explicit. In particular, we survey a number of statistical cultures for generating elections and their commonly used parameters.
Trends, Applications, and Challenges in Human Attention Modelling
Authors: Giuseppe Cartella; Marcella Cornia; Vittorio Cuculo; Alessandro D'Amelio; Dario Zanca; Giuseppe Boccignone; Rita Cucchiara
Venue: IJCAI 2024 (main)

Abstract: Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for providing support to artificial intelligence models that aim to solve problems in various domains, including image and video processing, vision-and-language applications, and language modelling. This survey offers a reasoned overview of recent efforts to integrate human attention mechanisms into contemporary deep learning models and discusses future research directions and challenges. For a comprehensive overview of the ongoing research, refer to our dedicated repository available at https://github.com/aimagelab/awesome-human-visual-attention.