Author name cluster

Lianlong Wu

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5 papers

1 author row

AAAI Conference 2024 System Paper

Generation of Visual Representations for Multi-Modal Mathematical Knowledge

Lianlong Wu
Seewon Choi
Daniel Raggi
Aaron Stockdill
Grecia Garcia Garcia
Fiorenzo Colarusso
Peter C.H. Cheng
Mateja Jamnik

In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.

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IJCAI Conference 2022 Conference Paper

Hybrid Learning System for Large-scale Medical Image Analysis

Zehua Cheng
Lianlong Wu

Adequate annotated data cannot always be satisfied in medical imaging applications. To address such a challenge, we would explore ways to reduce the quality and quantity of annotations requirements of the deep learning model by developing a hybrid learning system. We combined self-supervised learning, semi-supervised learning and weak-supervised learning to improve annotation utilization. Our primary research work on 2D medical image detection under poor annotation conditions has found that better regularization and adversarial loss can improve the robustness and performance with poor annotation conditions.

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AAAI Conference 2020 Conference Paper

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Yuhang Song
Andrzej Wojcicki
Thomas Lukasiewicz
Jianyi Wang
Abi Aryan
Zhenghua Xu
Mai Xu
Zihan Ding

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a speciﬁc game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-conﬁgurable social tree and ﬁve basic multi-agent reward schemes. Finally, we provide Python implementations of ﬁve state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https: //sites. google. com/view/arena-unity/.

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IJCAI Conference 2019 Conference Paper

Evolutionary Learning of Existential Rules

Lianlong Wu

Declarative rules such as Prolog and Datalog are common formalisms to express expert knowledge and are used in a number of systems. Since developing such rules is time-consuming and requires scarce expert knowledge, it is essential to develop algorithms for learning such rules. We address the problem of learning existential rules, a richer class of rules which found applications in many use-cases such as Semantic Web and Web Data Extraction. In particular, we concentrate on developing evolutionary learning algorithms for rule learning.

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AAAI Conference 2017 Short Paper

Evolutionary Machine Learning for RTS Game StarCraft

Lianlong Wu
Andrew Markham

Real-Time Strategy (RTS) games involve multiple agents acting simultaneously, and result in enormous state dimensionality. In this paper, we propose an abstracted and simplified model for the famous game StarCraft, and design a dynamic programming algorithm to solve the building order problem, which takes minimal time to achieve a specific target. In addition, Genetic Algorithms (GA) are used to find an optimal target for the opening stage.

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