GenAI4ELab/papercli-papers huggingface dataset:
VideoMaster: A Multimodal Micro Game Video Recreator
Authors: Yipeng Yu; Xiao Chen; Hui Zhan
Venue: IJCAI 2023 (main)

Abstract: To free human from laborious video production, this paper proposes the building of VideoMaster, a multimodal system equipped with four capabilities: highlight extraction, video describing, video dubbing and video editing. It extracts interesting episodes from long game videos, generates subtitles for each episode, reads the subtitles through synthesized speech, and finally re-creates a better short video through video editing. Notably, VideoMaster takes a combination of deep learning and traditional computer vision techniques to extract highlights with fine-to-coarse labels, utilizes a novel framework named PCSG-v (probabilistic context sensitive grammar for video) for video description generation, and imitates a target speaker's voice to read the description. To the best of our knowledge, VideoMaster is the first multimedia system that can automatically produce product-level micro-videos without heavy human annotation.
Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery
Authors: Yanhao Zhang; Fanyi Wang; Weixuan Sun; Jingwen Su; Peng Liu; Yaqian Li; Xinjie Feng; Zhengxia Zou
Venue: IJCAI 2023 (main)

Abstract: Image matting is a fundamental technique in visual understanding and has become one of the most significant capabilities in mobile phones. Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called "Matting Moments" that enables automatic image editing based on matting models in different scenarios. Coupled with accurate and refined matting subjects, our system provides visual element editing abilities and backend services for distribution and recommendation that respond to emotional expressions. Our system comprises three components: 1) photo content structuring, 2) data-driven matting engine, and 3) AIGC functions for generation, which automatically achieve diverse photo beautification in the gallery. This system offers a unified framework that guides consumers to obtain intelligent recommendations with beautifully generated contents, helping them enjoy the moments and memories of their present life.
IMPsys: An Intelligent Mold Processing System for Smart Factory
Authors: Xueyi Zhou; Yohan Na; Minju Bang; Dong-Kyu Chae
Venue: IJCAI 2023 (main)

Abstract: The explosive popularity of smart manufacturing has caught the attention of researchers in terms of intelligent mold processing and management. Machining mold components is a crucial step in the mold production process for many industries, which creates (e.g., cutting, drilling, and shaping a metal) the individual parts (e.g., core pins, ejector pins, cavities, slides, and lifters) that make up a mold used in manufacturing. We present IMPsys, an AI-based system that automatically explores machining jobs, infers their processing time and schedules them on machines, given numerous 3D modelling files of mold components. Our demo video can be found at: http://bit.ly/3EeKnyL.
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture
Authors: Yingjie Li; Weilu Gao; Cunxi Yu
Venue: IJCAI 2023 (main)

Abstract: Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests in leveraging ONNs into medical sensing, security screening, drug detection, and autonomous driving. However, due to the challenge of implementing reconfigurability, deploying multi-task learning (MTL) algorithms on ONNs requires re-building and duplicating the physical diffractive systems, which significantly degrades the energy and cost efficiency in practical application scenarios. This work presents a novel ONNs architecture, namely, RubikONNs, which utilizes the physical properties of optical systems to encode multiple feed-forward functions by physically rotating the hardware similarly to rotating a Rubik's Cube. To optimize MTL performance on RubikONNs, two domain-specific physics-aware training algorithms RotAgg and RotSeq are proposed. Our experimental results demonstrate more than 4x improvements in energy and cost efficiency with marginal accuracy degradation compared to the state-of-the-art approaches.
Imbalanced Node Classification Beyond Homophilic Assumption
Authors: Jie Liu; Mengting He; Guangtao Wang; Quoc Viet Hung Nguyen; Xuequn Shang; Hongzhi Yin
Venue: IJCAI 2023 (main)

Abstract: Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe performance degradation on classifying minority class nodes. Various imbalanced node classification methods have been proposed recently which construct synthetic nodes and edges w.r.t. minority classes to balance the label/topology distribution. However, they are all based on homophilic assumption that nodes of the same label tend to connect despite the widely existence of heterophilic edges in real-world graphs. Thus, they uniformly aggregate features from both homophilic and heterophilic neighbors and rely on feature similarity to generate synthetic edges, which cannot be applied to imbalanced graphs in high heterophily. To address this problem, we propose a novel GraphSANN for imbalanced node classification on both homophilic and heterophilic graphs. Firstly, we propose a unified feature mixer to generate synthetic nodes with both homophilic and heterophilic interpolation in a unified way. Next, by randomly sampling edges between synthetic nodes and existing nodes as candidata edges, we design an adaptive subgraph extractor to dynamically extract the contextual subgraphs of candidate edges with flexible ranges. Finally, we develop a multi-filter subgraph encoder which constructs multiple different filter channels to discriminatively aggregate neighbors’ information along the homophilic and heterophilic edges. Extensive experiments on eight benchmark datasets demonstrate the superiority of our model for imbalanced node classificaiton on both homophilic and heterophilic graphs.
Dual Prompt Learning for Continual Rain Removal from Single Images
Authors: Minghao Liu; Wenhan Yang; Yuzhang Hu; Jiaying Liu
Venue: IJCAI 2023 (main)

Abstract: Recent efforts have achieved remarkable progress on single image deraining on the stationary distributed data. However, catastrophic forgetting raises practical concerns when applying these methods to real applications, where the data distributions change constantly. In this paper, we investigate the continual learning issue for rain removal and develop a novel efficient continual learned deraining transformer. Different from the typical replay or regularization-based methods that increase overall training time or parameter space, our method relies on compact prompts which are learnable parameters, to maintain both task-invariant and task-specific knowledge. Our prompts are applied at both image and feature levels to leverage effectively transferred knowledge of images and features among different tasks. We conduct comprehensive experiments under widely-used rain removal datasets, where our proposed dual prompt learning consistently outperforms prior state-of-the-art methods. Moreover, we observe that, even though our method is designed for continual learning, it still achieves superior results on the stationary distributed data, which further demonstrates the effectiveness of our method. Our website is available at: http://liuminghao.com.cn/DPL/.
Multi-objective Search via Lazy and Efficient Dominance Checks
Authors: Carlos Hernández; William Yeoh; Jorge A. Baier; Ariel Felner; Oren Salzman; Han Zhang; Shao-Hung Chan; Sven Koenig
Venue: IJCAI 2023 (main)

Abstract: Multi-objective search can be used to model many real-world problems that require finding Pareto optimal paths from a specified start state to a specified goal state, while considering different costmetrics such as distance, time, and fuel. The performance of multi-objective search can be improved by making dominance checking—an operation necessary to determine whether or not a path dominates another—more efficient. This was shown in practice by BOA*, a state-of-the-art bi-objective search algorithm, which outperforms previously existing bi-objective search algorithms in part because it adopts a lazy approach towards dominance checking. EMOA*, a recent multi-objective search algorithm, generalizes BOA* to more-than-two objectives using AVL trees for dominance checking. In this paper, we first propose Linear-Time Multi-Objective A* (LTMOA*), an multi-objective search algorithm that implements a more efficient dominance checking than EMOA* using simple data structures like arrays. We then propose an even lazier approach towards dominance checking, and the resulting algorithm, LazyLTMOA*, distinguishes from EMOA* and LTMOA* by removing the dominance checking during node generation. Our experimental results show that LazyLTMOA* outperforms EMOA* by up to an order of magnitude in terms of runtime.
Bayesian Federated Learning: A Survey
Authors: Longbing Cao; Hui Chen; Xuhui Fan; Joao Gama; Yew-Soon Ong; Vipin Kumar
Venue: IJCAI 2023 (main)

Abstract: Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.
Certified Policy Verification and Synthesis for MDPs under Distributional Reach-Avoidance Properties
Authors: S. Akshay; Krishnendu Chatterjee; Tobias Meggendorfer; Đorđe Žikelić
Venue: IJCAI 2024 (main)

Abstract: Markov Decision Processes (MDPs) are a classical model for decision making in the presence of uncertainty. Often they are viewed as state transformers with planning objectives defined with respect to paths over MDP states. An increasingly popular alternative is to view them as distribution transformers, giving rise to a sequence of probability distributions over MDP states. For instance, reachability and safety properties in modeling robot swarms or chemical reaction networks are naturally defined in terms of probability distributions over states. Verifying such distributional properties is known to be hard and often beyond the reach of classical state-based verification techniques. In this work, we consider the problems of certified policy (i.e. controller) verification and synthesis in MDPs under distributional reach-avoidance specifications. By certified we mean that, along with a policy, we also aim to synthesize a (checkable) certificate ensuring that the MDP indeed satisfies the property. Thus, given the target set of distributions and an unsafe set of distributions over MDP states, our goal is to either synthesize a certificate for a given policy or synthesize a policy along with a certificate, proving that the target distribution can be reached while avoiding unsafe distributions. To solve this problem, we introduce the novel notion of distributional reach-avoid certificates and present automated procedures for (1) synthesizing a certificate for a given policy, and (2) synthesizing a policy together with the certificate, both providing formal guarantees on certificate correctness. Our experimental evaluation demonstrates the ability of our method to solve several non-trivial examples, including a multi-agent robot-swarm model, to synthesize certified policies and to certify existing policies.
Truth Table Net: Scalable, Compact & Verifiable Neural Networks with a Dual Convolutional Small Boolean Circuit Networks Form
Authors: Adrien Benamira; Thomas Peyrin; Trevor Yap; Tristan Guérand; Bryan Hooi
Venue: IJCAI 2024 (main)

Abstract: We introduce "Truth Table net"' (TTnet), a novel Deep Neural Network (DNN) architecture designed to provide excellent scalability/compactness trade-offs among DNNs, allowing in turn to tackle the DNN challenge of fast formal verification. TTnet is constructed using Learning Truth Table (LTT) filters, analogous to how a Deep Convolutional Neural Network (DCNN) is built upon convolutional filters. The differentiable LTT filters are unique by their dual form: they are both a neural network-based function and a small-sized truth table that can be computed within a practical time frame. This characteristic guarantees, by design and independently of the overall architecture, the ability to practically extract an efficient (in terms of the number of logical gates) and functionally equivalent Conjunctive Normal Form (CNF) Boolean logic gate implementation. This CNF circuit is even optimal when the LTT truth table's input bit size n < 12. In particular, TTnet architecture is the first differentiable DNN with as dual form a compact logic gate representation that can scale to datasets larger than CIFAR-10: we achieve an accuracy of 41% on the ImageNet dataset while ensuring that each LTT filter truth table is fully computable within 2^{16} operations. We further compare the compactness and scalability performances of TTnet Boolean logic circuit representation to state-of-the-art differentiable logic DNNs across tabular, MNIST, and CIFAR-10 datasets. We emphasize that TTnet is the first solution to the open problem of designing differentiable convolutional neural networks with an exact dual logic gate circuit representation, bridging the gap between symbolic AI and trainable DCNNs. Finally, as improving DNNs compactness in Boolean logic circuit form reduces the complexity of their formal verification, we demonstrate TTnet effectiveness in exact sound and complete formal verification. Notably, our model achieves robustness verification in 10ms vs 100s for traditional state-of-the-art DNNs solvers.
AutoAgents: A Framework for Automatic Agent Generation
Authors: Guangyao Chen; Siwei Dong; Yu Shu; Ge Zhang; Jaward Sesay; Börje Karlsson; Jie Fu; Yemin Shi
Venue: IJCAI 2024 (main)

Abstract: Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents.
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Authors: Yiqun Chen; Hangyu Mao; Jiaxin Mao; Shiguang Wu; Tianle Zhang; Bin Zhang; Wei Yang; Hongxing Chang
Venue: IJCAI 2024 (main)

Abstract: Centralized Training with Decentralized Execution (CTDE) has emerged as a widely adopted paradigm in multi-agent reinforcement learning, emphasizing the utilization of global information for learning an enhanced joint Q-function or centralized critic. In contrast, our investigation delves into harnessing global information to directly enhance individual Q-functions or individual actors. Notably, we discover that applying identical global information universally across all agents proves insufficient for optimal performance. Consequently, we advocate for the customization of global information tailored to each agent, creating agent-personalized global information to bolster overall performance. Furthermore, we introduce a novel paradigm named Personalized Training with Distilled Execution (PTDE), wherein agent-personalized global information is distilled into the agent's local information. This distilled information is then utilized during decentralized execution, resulting in minimal performance degradation. PTDE can be seamless integrated with state-of-the-art algorithms, leading to notable performance enhancements across diverse benchmarks, including the SMAC benchmark, Google Research Football (GRF) benchmark, and Learning to Rank (LTR) task.
Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition
Authors: Dylan Cope; Peter McBurney
Venue: IJCAI 2024 (main)

Abstract: In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community. This observation led to research into Zero-Shot Coordination (ZSC) for learning communication strategies that are robust to agents not encountered during training. However, ZSC typically assumes that no prior data is available about the agents that will be encountered in the zero-shot setting. In many cases, this presents an unnecessarily hard problem and rules out communication via preestablished conventions. We propose a novel AI challenge called a Cooperative Language Acquisition Problem (CLAP) in which the ZSC assumptions are relaxed by allowing a 'joiner' agent to learn from a dataset of interactions between agents in a target community. We propose and compare two methods for solving CLAPs: Behaviour Cloning (BC), and Emergent Communication pretraining and Translation Learning (ECTL), in which an agent is trained in self-play with EC and then learns to translate between an emergent protocol and the target community's protocol.
Improving Multi-agent Reinforcement Learning with Stable Prefix Policy
Authors: Yue Deng; Zirui Wang; Yin Zhang
Venue: IJCAI 2024 (main)

Abstract: In multi-agent reinforcement learning (MARL), the epsilon-greedy method plays an important role in balancing exploration and exploitation during the decision-making process in value-based algorithms. However, the epsilon-greedy exploration process will introduce conservativeness when calculating the expected state value when the agents are more in need of exploitation during the approximate policy convergence, which may result in a suboptimal policy convergence. Besides, eliminating the epsilon-greedy algorithm leaves no exploration and may lead to unacceptable local optimal policies. To address this dilemma, we use the previously collected trajectories to construct a Monte-Carlo Trajectory Tree, so that an existing optimal template, a sequence of state prototypes, can be planned out. The agents start by following the planned template and act according to the policy without exploration, Stable Prefix Policy. The agents will adaptively dropout and begin to explore by following the epsilon-greedy method when the policy still needs exploration. We scale our approach to various value-based MARL methods and empirically verify our method in a cooperative MARL task, SMAC benchmarks. Experimental results demonstrate that our method achieves not only better performance but also faster convergence speed than baseline algorithms within early time steps.
Scalable Mechanism Design for Multi-Agent Path Finding
Authors: Paul Friedrich; Yulun Zhang; Michael Curry; Ludwig Dierks; Stephen McAleer; Jiaoyang Li; Tuomas Sandholm; Sven Seuken
Venue: IJCAI 2024 (main)

Abstract: Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This problem is computationally complex, especially when dealing with large numbers of agents, as is common in realistic applications like autonomous vehicle coordination. Finding an optimal solution is often computationally infeasible, making the use of approximate, suboptimal algorithms essential. Adding to the complexity, agents might act in a self-interested and strategic way, possibly misrepresenting their goals to the MAPF algorithm if it benefits them. Although the field of mechanism design offers tools to align incentives, using these tools without careful consideration can fail when only having access to approximately optimal outcomes. In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms. We test our mechanisms on realistic MAPF domains with problem sizes ranging from dozens to hundreds of agents. We find that they improve welfare beyond a simple baseline.
Endogenous Energy Reactive Modules Games: Modelling Side Payments among Resource-Bounded Agents
Authors: Julian Gutierrez; David Hyland; Muhammad Najib; Giuseppe Perelli; Michael Wooldridge
Venue: IJCAI 2024 (main)

Abstract: We introduce Energy Reactive Modules Games (ERMGs), an extension of Reactive Modules Games (RMGs) in which actions incur an energy cost (which may be positive or negative), and the choices that players make are restricted by the energy available to them. In ERMGs, each action is associated with an energy level update, which determines how their energy level is affected by the performance of the action. In addition, agents are provided with an initial energy allowance. This allowance plays a crucial role in shaping an agent’s behaviour, as it must be taken into consideration when one is determining their strategy: agents may only perform actions if they have the requisite energy. We begin by studying rational verification for ERMGs and then introduce Endogenous ERMGs, where agents can choose to transfer their energy to other agents. This exchange may enable equilibria that are impossible to achieve without such transfers. We study the decision problem of whether a stable outcome exists under both the Nash equilibrium and Core solution concepts.