GenAI4ELab/papercli-papers huggingface dataset:
Multi-Agent Advisor Q-Learning (Extended Abstract)
Authors: Sriram Ganapathi Subramanian; Matthew E. Taylor; Kate Larson; Mark Crowley
Venue: IJCAI 2023 (main)

Abstract: In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. We provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning-based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework (Extended Abstract)
Authors: Evana Gizzi; Lakshmi Nair; Sonia Chernova; Jivko Sinapov
Venue: IJCAI 2023 (main)

Abstract: Creative Problem Solving (CPS) is a sub-area within artificial intelligence that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning in AI, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways, remains a challenge. To stimulate further research in CPS, we contribute a definition and a framework of CPS, which we use to categorize existing AI methods in this field. We conclude our survey with open research questions, and suggested future directions.
Ordinal Maximin Share Approximation for Goods (Extended Abstract)
Authors: Hadi Hosseini; Andrew Searns; Erel Segal-Halevi
Venue: IJCAI 2023 (main)

Abstract: In fair division of indivisible goods, l-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the l least preferred bundles. Most existing works aim to guarantee to all agents a constant fraction of their 1-out-of-n MMS. But this guarantee is sensitive to small perturbation in agents' cardinal valuations. We consider a more robust approximation notion, which depends only on the agents' ordinal rankings of bundles. We prove the existence of l-out-of-floor((l+1/2)n) MMS allocations of goods for any integer l greater than or equal to 1, and present a polynomial-time algorithm that finds a 1-out-of-ceiling(3n/2) MMS allocation when l = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any l > 1.
On Tackling Explanation Redundancy in Decision Trees (Extended Abstract)
Authors: Yacine Izza; Alexey Ignatiev; Joao Marques-Silva
Venue: IJCAI 2023 (main)

Abstract: Claims about the interpretability of decision trees can be traced back to the origins of machine learning (ML). Indeed, given some input consistent with a decision tree's path, the explanation for the resulting prediction consists of the features in that path. Moreover, a growing number of works propose the use of decision trees, and of other so-called interpretable models, as a possible solution for deploying ML models in high-risk applications. This paper overviews recent theoretical and practical results which demonstrate that for most decision trees, tree paths exhibit so-called explanation redundancy, in that logically sound explanations can often be significantly more succinct than what the features in the path dictates. More importantly, such decision tree explanations can be computed in polynomial-time, and so can be produced with essentially no effort other than traversing the decision tree. The experimental results, obtained on a large range of publicly available decision trees, support the paper's claims.
Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)
Authors: Rana Tallal Javed; Osama Nasir; Melania Borit; Loïs Vanhée; Elias Zea; Shivam Gupta; Ricardo Vinuesa; Junaid Qadir
Venue: IJCAI 2023 (main)

Abstract: This study explores the topics and trends of teaching AI ethics in higher education, using Latent Dirichlet Allocation as the analysis tool. The analyses included 166 courses from 105 universities around the world. Building on the uncovered patterns, we distil a model of current pedagogical practice, the BAG model (Build, Assess, and Govern), that combines cognitive levels, course content, and disciplines. The study critically assesses the implications of this teaching paradigm and challenges practitioners to reflect on their practices and move beyond stereotypes and biases.
Data-Informed Knowledge and Strategies (Extended Abstract)
Authors: Junli Jiang; Pavel Naumov
Venue: IJCAI 2023 (main)

Abstract: The article proposes a new approach to reasoning about knowledge and strategies in multiagent systems. It emphasizes data, not agents, as the source of strategic knowledge. The approach brings together Armstrong's functional dependency expression from database theory, a data-informed knowledge modality based on a recent work by Baltag and van Benthem, and a newly proposed data-informed strategy modality. The main technical result is a sound and complete logical system that describes the interplay between these three logical operators.
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters (Extended Abstract)
Authors: Kebing Jin; Hankz Hankui Zhuo; Zhanhao Xiao; Hai Wan; Subbarao Kambhampati
Venue: IJCAI 2023 (main)

Abstract: Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems, especially when the problems contain obstacles and non-linear numeric effects. In this work, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend the syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to the next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem.
Rethinking Formal Models of Partially Observable Multiagent Decision Making (Extended Abstract)
Authors: Vojtěch Kovařík; Martin Schmid; Neil Burch; Michael Bowling; Viliam Lisý
Venue: IJCAI 2023 (main)

Abstract: Multiagent decision-making in partially observable environments is usually modelled as either an extensive-form game (EFG) in game theory or a partially observable stochastic game (POSG) in multiagent reinforcement learning (MARL). One issue with the current situation is that while most practical problems can be modelled in both formalisms, the relationship of the two models is unclear, which hinders the transfer of ideas between the two communities. A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition. To solve the first issue, we introduce factored-observation stochastic games (FOSGs), a minor modification of the POSG formalism which distinguishes between private and public observation and thereby greatly simplifies decomposition. To remedy the second issue, we show that FOSGs and POSGs are naturally connected to EFGs: by "unrolling" a FOSG into its tree form, we obtain an EFG. Conversely, any perfect-recall timeable EFG corresponds to some underlying FOSG in this manner. Moreover, this relationship justifies several minor modifications to the classical EFG formalization that recently appeared as an implicit response to the model's issues with decomposition. Finally, we illustrate the transfer of ideas between EFGs and MARL by presenting three key EFG techniques -- counterfactual regret minimization, sequence form, and decomposition -- in the FOSG framework.
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning (Extended Abstract)
Authors: Xiaoteng Ma; Shuai Ma; Li Xia; Qianchuan Zhao
Venue: IJCAI 2023 (main)

Abstract: Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is variance, while it penalizes the upside volatility as much as the downside part. Instead, the (downside) semivariance, which captures negative deviation of a random variable under its mean, is more suitable for risk-averse proposes. This paper aims at optimizing the mean-semivariance (MSV) criterion in reinforcement learning w.r.t. steady reward distribution. Since semivariance is time-inconsistent and does not satisfy the standard Bellman equation, the traditional dynamic programming methods are inapplicable to MSV problems directly. To tackle this challenge, we resort to Perturbation Analysis (PA) theory and establish the performance difference formula for MSV. We reveal that the MSV problem can be solved by iteratively solving a sequence of RL problems with a policy-dependent reward function. Further, we propose two on-policy algorithms based on the policy gradient theory and the trust region method. Finally, we conduct diverse experiments from simple bandit problems to continuous control tasks in MuJoCo, which demonstrate the effectiveness of our proposed methods.
Learning to Design Fair and Private Voting Rules (Extended Abstract)
Authors: Farhad Mohsin; Ao Liu; Pin-Yu Chen; Francesca Rossi; Lirong Xia
Venue: IJCAI 2023 (main)

Abstract: Voting is used widely to aggregate preferences to make a collective decision. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. First, we introduce a novel notion of group fairness and adopt the existing notion of local differential privacy. We then evaluate the level of group fairness in several existing voting rules, as well as the trade-offs between fairness and privacy, showing that it is not possible to always obtain maximal economic efficiency with high fairness. Then, we present both a machine learning and a constrained optimization approach to design new voting rules that are fair while maintaining a high level of economic efficiency. Finally, we empirically examine the effect of adding noise to create local differentially private voting rules and discuss the three-way trade-off between economic efficiency, fairness, and privacy.
A Computational Model of Ostrom's Institutional Analysis and Development Framework (Extended Abstract)
Authors: Nieves Montes; Nardine Osman; Carles Sierra
Venue: IJCAI 2023 (main)

Abstract: Ostrom's Institutional Analysis and Development (IAD) framework represents a comprehensive theoretical effort to identify and outline the variables that determine the outcome in any social interaction. Taking inspiration from it, we define the Action Situation Language (ASL), a machine-readable logical language to express the components of a multiagent interaction, with a special focus on the rules adopted by the community. The ASL is complemented by a game engine that takes an interaction description as input and automatically grounds its semantics as an Extensive-Form Game (EFG), which can be readily analysed using standard game-theoretical solution concepts. Overall, our model allows a community of agents to perform what-if analysis on a set of rules being considered for adoption, by automatically connecting rule configurations to the outcomes they incentivize.
Proofs and Certificates for Max-SAT (Extended Abstract)
Authors: Matthieu Py; Mohamed Sami Cherif; Djamal Habet
Venue: IJCAI 2023 (main)

Abstract: In this paper, we present a tool, called MS-Builder, which generates certificates for the Max-SAT problem in the particular form of a sequence of equivalence-preserving transformations. To generate a certificate, MS-Builder iteratively calls a SAT oracle to get a SAT resolution refutation which is handled and adapted into a sound refutation for Max-SAT. In particular, the size of the computed Max-SAT refutation is linear with respect to the size of the initial refutation if it is semi-read-once, tree-like regular, tree-like or semi-tree-like. Additionally, we propose an extendable tool, called MS-Checker, able to verify the validity of any Max-SAT certificate using Max-SAT inference rules.
Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation (Extended Abstract)
Authors: Eugénio Ribeiro; Ricardo Ribeiro; David Martins de Matos
Venue: IJCAI 2023 (main)

Abstract: From the perspective of a dialog system, the identification of the intention behind the segments in a dialog is important, as it provides cues regarding the information present in the segments and how they should be interpreted. The ISO 24617-2 standard for dialog act annotation defines a hierarchically organized set of general-purpose communicative functions that correspond to different intentions that are relevant in the context of a dialog. In this paper, we explore the automatic recognition of these functions. To do so, we propose to adapt existing approaches to dialog act recognition, so that they can deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Additionally, we rely on transfer learning processes to address the data scarcity problem. Our experiments on the DialogBank show that this approach outperforms both flat and hierarchical approaches based on multiple classifiers and that each of its components plays an important role in the recognition of general-purpose communicative functions.
Memory-Limited Model-Based Diagnosis (Extended Abstract)
Authors: Patrick Rodler
Venue: IJCAI 2023 (main)

Abstract: Model-based diagnosis is a principled and broadly applicable AI-based approach to tackle debugging problems in a wide range of areas including software, knowledge bases, circuits, cars, and robots. Whenever the sound and complete computation of fault explanations in a given preference order (e.g., cardinality or probability) is required, all existing diagnosis algorithms suffer from an exponential space complexity. This can prevent their application on memory-restricted devices and for memory-intensive problem cases. As a remedy, we propose RBF-HS, a diagnostic search based on Korf’s seminal RBFS algorithm which can enumerate an arbitrary ﬁxed number of fault explanations in best-ﬁrst order within linear space bounds, without sacriﬁcing other desirable properties. Evaluations on real-world diagnosis cases show that RBF-HS, when used to compute minimum-cardinality fault explanations, in most cases saves substantial space while requiring only reasonably more or even less time than Reiter’s HS-Tree, one of the most inﬂuential diagnostic algorithms with the same properties.
Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract)
Authors: Loris Roveda; Andrea Testa; Asad Ali Shahid; Francesco Braghin; Dario Piga
Venue: IJCAI 2023 (main)

Abstract: Physical human-robot collaboration is increasingly required in many contexts. To implement an effective collaboration, the robot should be able to recognize the human’s intentions and guarantee safe and adaptive behavior along the intended motion directions. The robot-control strategies with such attributes are particularly demanded in the industrial field. Indeed, with this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in physical human-robot collaboration (pHRC) tasks. A Cartesian impedance control loop is designed to implement decoupled compliant robot dynamics. The impedance control parameters (i.e., setpoint and damping parameters) are then optimized online in order to maximize the performance of the pHRC. For this purpose, an ensemble of neural networks is designed to learn the modeling of the human-robot interaction dynamics while capturing the associated uncertainties. The derived modeling is then exploited by the model predictive controller (MPC), enhanced with stability guarantees by means of Lyapunov constraints. The MPC is solved by making use of a Q-Learning method that, in its online implementation, uses an actor-critic algorithm to approximate the exact solution. Indeed, the Q-learning method provides an accurate and highly efficient solution (in terms of computational time and resources). The proposed approach has been validated through experimental tests, in which a Franka EMIKA panda robot has been used as a test platform.
A Survey of Methods for Automated Algorithm Configuration (Extended Abstract)
Authors: Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney
Venue: IJCAI 2023 (main)

Abstract: Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There are currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. Existing AC literature is classified and characterized by the provided taxonomies.
Motion Planning Under Uncertainty with Complex Agents and Environments via Hybrid Search (Extended Abstract)
Authors: Daniel Strawser; Brian Williams
Venue: IJCAI 2023 (main)

Abstract: As autonomous systems tackle more real-world situations, mission success oftentimes cannot be guaranteed and the planner must reason about the probability of failure. Unfortunately, computing a trajectory that satisfies mission goals while constraining the probability of failure is difficult because of the need to reason about complex, multidimensional probability distributions. Recent methods have seen success using chance-constrained, model-based planning. We argue there are two main drawbacks to these approaches. First, current methods suffer from an inability to deal with expressive environment models such as 3D non-convex obstacles. Second, most planners rely on considerable simplifications when computing trajectory risk including approximating the agent's dynamics, geometry, and uncertainty. We apply hybrid search to the risk-bound, goal-directed planning problem. The hybrid search consists of a region planner and a trajectory planner. The region planner makes discrete choices by reasoning about geometric regions that the agent should visit in order to accomplish its mission. In formulating the region planner, we propose landmark regions that help produce obstacle-free paths. The region planner passes paths through the environment to a trajectory planner; the task of the trajectory planner is to optimize trajectories that respect the agent's dynamics and the user's desired risk of mission failure. We discuss three approaches to modeling trajectory risk: a CDF-based approach, a sampling-based collocation method, and an algorithm named Shooting Method Monte Carlo. A variety of 2D and 3D test cases are presented in the full paper including a linear case, a Dubins car model, and an underwater autonomous vehicle. The method is shown to outperform other methods in terms of speed and utility of the solution. Additionally, the models of trajectory risk are shown to better approximate risk in simulation.
Incremental Event Calculus for Run-Time Reasoning (Extended Abstract)
Authors: Efthimis Tsilionis; Alexander Artikis; Georgios Paliouras
Venue: IJCAI 2023 (main)

Abstract: We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTEC_inc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTEC_inc deals with delays and revisions in a more efficient way, by updating only the affected queries. We compare RTEC_inc and RTEC experimentally using real-world and synthetic datasets. The results are compatible with our complexity analysis and show that RTEC_inc outperforms RTEC in many practical cases.
Ethical By Designer - How to Grow Ethical Designers of Artificial Intelligence (Extended Abstract)
Authors: Loïs Vanhée; Melania Borit
Venue: IJCAI 2023 (main)

Abstract: Ethical concerns regarding Artificial Intelligence technology have fueled discussions around the ethics training received by its designers. Training designers for ethical behaviour, understood as habitual application of ethical principles in any situation, can make a significant difference in the practice of research, development, and application of AI systems. Building on interdisciplinary knowledge and practical experience from computer science, moral psychology, and pedagogy, we propose a functional way to provide this training.
A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract)
Authors: Stylianos Loukas Vasileiou; William Yeoh; Son Tran; Ashwin Kumar; Michael Cashmore; Daniele Magazzeni
Venue: IJCAI 2023 (main)

Abstract: In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent's model. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes.