United States

This paper introduces a general framework for generate-and-test-based solvers for epistemic logic programs that can be instantiated with different generate and test programs, and it provides sufficient conditions on those programs for the correctness of the solvers built using this framework. It also introduces a new generator program that incorporates the propagation of epistemic consequences and shows that this can exponentially reduce the number of candidates that need to be tested while only incurring a linear overhead. We implement a new solver based on these theoretical findings and experimentally show that it outperforms existing solvers by achieving a ~3.3x speed-up and solving 87\% more instances on well-known benchmarks.

AAAI 2025

Solving Epistemic Logic Programs Using Generate-and-Test with Propagation

logic programming

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Datalog is a logic programming language widely used in knowledge representation and reasoning (KRR), program analysis, and social media mining due to its expressiveness and high performance. 
Traditionally, Datalog engines use either row-oriented or column-oriented storage. Engines like VLog and Nemo favor column-oriented storage for efficiency on limited-resource machines, while row-oriented engines like Soufflé use advanced datastructures with locking to perform better on multi-core CPUs.
The advent of modern datacenter GPUs, such as the NVIDIA H100 with its ability to run over 16k threads simultaneously and high memory bandwidth, has reopened the debate on which storage layout is more effective. This paper presents the first column-oriented Datalog engines tailored to the strengths of modern GPUs. We present VFLog, a CUDA-based Datalog runtime library with a column-oriented GPU datastructure that supports all necessary relational algebra operations. Our results demonstrate over $200\times$ performance gains over SOTA CPU-based column-oriented Datalog engines and a $2.5\times$ speedup over GPU Datalog engines in various workloads, including KRR.

Column-Oriented Datalog on the GPU

Learning control policy from continuous action space by visual observations is a fundamental and challenging task in reinforcement learning (RL). An essential problem is how to accurately map the high-dimensional images to the optimal actions by the policy network. Traditional decision-making modules output actions solely based on the current observation, while the distributions of optimal actions are dependent on specific tasks and cannot be known priorly, which increases the learning difficulty. To make the learning easier, we analyze the action characteristics in several control tasks, and propose Reinforcement Learning with Residual Action (ResAct) to explicitly model the adjustments of actions based on the differences between adjacent observations, rather than learning actions directly from observations. The method just redefines the output of the policy network, and doesn’t introduce any prior assumption to constrain or simplify the vanilla control problem. Extensive experiments on DeepMind Control Suite and CARLA demonstrate that the method could improve different RL baselines significantly, and achieve state-of-the-art performance.

Visual Reinforcement Learning with Residual Action

Proportional representation plays a crucial role in electoral systems. In ordinal elections, where voters rank candidates based on their preferences, the Single Transferable Vote (STV) is the most widely used proportional voting method. STV is considered proportional because it satisfies an axiom requiring that large enough "solid coalitions" of voters are adequately represented. Using real-world data from local Scottish elections, we observe that solid coalitions of the required size rarely occur in practice. This observation challenges the importance of proportionality axioms and raises the question of how the proportionality of voting methods can be assessed beyond their axiomatic performance. We address these concerns by developing quantitative measures of proportionality. We apply these measures to evaluate the proportionality of voting rules on real-world election data. Besides STV, we consider SNTV, the Expanding Approvals Rule, and Sequential Ranked-Choice Voting. We also study the effects of ballot truncation by artificially completing truncated ballots and comparing the proportionality of outcomes under complete and truncated ballots.

Proportional Representation in Practice: Quantifying Proportionality in Ordinal Elections

The effectiveness of satisfiability solvers significantly depends on the quality of the encoding of a given problem into conjunctive normal form. Cardinality constraints are prevalent in numerous problems, prompting the development and study of various encoding types. This paper presents a novel approach to optimizing cardinality constraint encodings by examining the impact of literal orderings within the constraints, independent of the encoding type. Unlike traditional metrics, such as formula size and propagation power, our method leverages formula structure to adjust the meanings of auxiliary variables within encodings, thereby enhancing a solver's learning capabilities. Experimental evaluations on benchmarks from the maximum satisfiability competition reveal that good literal orderings can be more crucial than the choice of encoding type. Moreover, our automatically-generated literal orderings consistently improve performance across all encoding types, demonstrating the robustness of our approach.

The Impact of Literal Sorting on Cardinality Constraint Encodings

Multi-view clustering (MVC) methods have gained significant attention, most of which adopt centralized data settings. Real-world multi-view data may probably be collected and stored by different organizations, which increases the challenge of practical MVC deployment and motivates the emergence of federated MVC (FMVC).  However, existing FMVC methods require post-processing to obtain clustering labels and struggle to explore complementary and consistent information between multi-view data located in different entities. To address these issues, we propose a novel Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization (SFOMVC-TR), which incorporates anchor graph to improve the clustering efficiency and scalability on high-dimensional data.  SFOMVC-TR performs embedding learning on the anchor graph, and by applying sparse constraints to the projection matrix, it effectively eliminates the redundant information in anchor graphs, thereby allowing clustering in one step. We further introduce a third-order tensor to capture complementary multi-view features and consistent information simultaneously. A federated optimization algorithm is developed to support collaborative and privacy-preserving training under the coordination of a server. Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method.

Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization

In this blue sky paper, we seek to stimulate the research community to pursue important new as well as existing (unsolved) AI problems in the context of a challenging, often ignored, socio-sensitive application domain. We outline the key challenges in conducting elections credibly in leading democracies around the world today and identify our vision of a path forward with an overarching goal to increase voter participation with a two-pronged approach of AI-lead technological innovations and interdisciplinary community building. On the technology front, we envisage the need to transform Collation and Distribution of election information, and promote its Comprehensibility for users understanding and trust (CDC). On the community front, we need to invigorate the multi-disciplinary community consisting of, but not limited to, researchers in AI, security, journalism, political science, sociology, and business, to PROMote AI’s Safe usage for Elections (PROMISE) with best-practices. This work is informed by our interdisciplinary research as well as experience in conducting three workshops at leading AI conferences and the AI Magazine special issue on {\em AI and Elections}.

A Vision for Reinventing Credible Elections with Artificial Intelligence

This paper addresses the challenges of computational accountability in autonomous systems, particularly in Autonomous Vehicles (AVs), where safety and efficiency often conflict. We begin by examining current approaches such as cost minimization, reward maximization, human-centered approaches, and ethical frameworks, noting their limitations addressing these challenges. Foreseeability is a central concept in tort law that limits the accountability and legal liability of an actor to a reasonable scope. Yet, current data-driven methods to determine foreseeability are rigid, ignore uncertainty, and depend on simulation data. In this work, we advocate for a new computational approach to establish foreseeability of autonomous systems based on the legal “BPL” formula. We provide open research challenges, using fully autonomous vehicles as a motivating example, and call for researchers to help autonomous systems make accountable decisions in safety-critical scenarios.

Towards Computational Foreseeability

This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitations in the current AI pipeline and its WEIRD* representation, such as lack of data diversity, biases in model performance, and narrow evaluation metrics. We also focus on the need for diverse representation among the developers of these systems, as well as incentives that are not skewed toward certain groups. We highlight opportunities to develop AI systems that are for everyone (with diverse stakeholders in mind), with everyone (inclusive of diverse data and annotators), and by everyone (designed and developed by a globally diverse workforce).


*WEIRD = an acronym coined by Joseph Henrich to highlight the coverage limitations of many psychological studies, referring to populations that are Western, Educated, Industrialized, Rich, and Democratic; while we do not fully adopt this term for AI, as its current scope does not perfectly align with the WEIRD dimensions, we believe that today's AI has a similarly "weird" coverage, particularly in terms of who is involved in its development and who benefits from it.

Why AI Is WEIRD and Shouldn't Be This Way: Towards AI for Everyone, with Everyone, by Everyone

Ensuring that AI systems do what we, as humans, actually want them to do, is one of the biggest open research challenges in AI alignment and safety. My research seeks to directly address this challenge by enabling AI systems to interact with humans to learn aligned and robust behaviors. The way in which robots and other AI systems behave is often the result of optimizing a reward function. However, manually designing good reward functions is highly challenging and error prone, even for domain experts. Consider trying to write down a reward function that describes good driving behavior or how you like your bed made in the morning. While reward functions for these tasks are difficult to manually specify, human feedback in the form of demonstrations or preferences are often much easier to obtain. However, human data is often difficult to interpret, due to ambiguity and noise. Thus, it is critical that AI systems take into account epistemic uncertainty over the human's true intent. My talk will give an overview of my lab's progress along the following fundamental research areas: (1) efficiently maintaining uncertainty over human intent, (2) directly optimizing behavior to be robust to uncertainty over human intent, and (3) actively querying for additional human input to reduce uncertainty over human intent.

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Evaluating Epistemic Logic Programs via Answer Set Programming with Quantifiers

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Column-Oriented Datalog on the GPU

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Evaluating Epistemic Logic Programs via Answer Set Programming with Quantifiers

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