Miracle-dz/UniFAR-FTUs huggingface dataset:
From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots

In this paper, we consider a first step to bridge a gap in coordinating task planning robots. Specifically, we study the automatic construction of languages that are maximally flexible while being sufficiently explicative for coordination. To this end, we view language as a machinery for specifying temporal-state constraints of plans. Such a view enables us to reverse-engineer a language from the ground up by mapping these composable constraints to words. Our language expresses a plan for any given task as a"plan sketch"to convey just-enough details while maximizing the flexibility to realize it, leading to robust coordination with optimality guarantees among other benefits. We formulate and analyze the problem, provide an approximate solution, and validate the advantages of our approach under various scenarios to shed light on its applications.
Towards a Logical Account for Human-Aware Explanation Generation in Model Reconciliation Problems

A model reconciliation problem focuses on producing explanations for human users who have varying expectations of the AI agent. This research explores the development of a general framework for generating human-aware explanations in such problems. We face two primary challenges: creating an expressive and efficient framework that generates personalized and persuasive explanations for users, and interactively incorporating users’ knowledge, beliefs, and preferences into the explanation process. We propose that a logic-based framework is well-suited for identifying, representing, and offering robust and tailored explanations to human users in model reconciliation scenarios.
Reward-Machine-Guided, Self-Paced Reinforcement Learning

Self-paced reinforcement learning (RL) aims to improve the data efficiency of learning by automatically creating sequences, namely curricula, of probability distributions over contexts. However, existing techniques for self-paced RL fail in long-horizon planning tasks that involve temporally extended behaviors. We hypothesize that taking advantage of prior knowledge about the underlying task structure can improve the effectiveness of self-paced RL. We develop a self-paced RL algorithm guided by reward machines, i.e., a type of finite-state machine that encodes the underlying task structure. The algorithm integrates reward machines in 1) the update of the policy and value functions obtained by any RL algorithm of choice, and 2) the update of the automated curriculum that generates context distributions. Our empirical results evidence that the proposed algorithm achieves optimal behavior reliably even in cases in which existing baselines cannot make any meaningful progress. It also decreases the curriculum length and reduces the variance in the curriculum generation process by up to one-fourth and four orders of magnitude, respectively.
PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning

Despite many breakthroughs in recent years, it is still hard for MultiAgent Reinforcement Learning (MARL) algorithms to directly solve complex tasks in MultiAgent Systems (MASs) from scratch. In this work, we study how to use Automatic Curriculum Learning (ACL) to reduce the number of environmental interactions required to learn a good policy. In order to solve a difficult task, ACL methods automatically select a sequence of tasks (i.e., curricula). The idea is to obtain maximum learning progress towards the final task by continuously learning on tasks that match the current capabilities of the learners. The key question is how to measure the learning progress of the learner for better curriculum selection. We propose a novel ACL framework, PrOgRessive mulTiagent Automatic curricuLum (PORTAL), for MASs. PORTAL selects curricula according to two critera: 1) How difficult is a task, relative to the learners’ current abilities? 2) How similar is a task, relative to the final task? By learning a shared feature space between tasks, PORTAL is able to characterize different tasks based on the distribution of features and select those that are similar to the final task. Also, the shared feature space can effectively facilitate the policy transfer between curricula. Experimental results show that PORTAL can train agents to master extremely hard cooperative tasks, which can not be achieved with previous state-of-the-art MARL algorithms.
Models of Anxiety for Agent Deliberation: The Benefits of Anxiety-Sensitive Agents

Anxiety is one of the most critical sources of harm to psychological wellbeing, tied to an array of issues, from discomfort and maladaptive coping to severe pathological disorders –making of anxiety one of the largest economic and social healthcare expenses. AI systems are not neutral to the exposure of individuals and societies to anxiety, and the current emphasis on performance-optimization of current AI systems arguably sets a pathway for a systemic rise of anxiety. As a response to this trend, towards further increasing the human-centeredness of existing applications, this paper is dedicated to depicting the landscape of open challenges, high-impact applications, and promising solutions for designing anxiety-sensitive agents. This paper first circumvents the key components of anxiety through a summary of the extensive psychology literature on anxiety; then shows the feasibility of building agent-based models by putting forward an example of a logical model of anxiety; and last, examines current research fields through the lens of anxiety, highlighting categories of prospective applications and techniques which stand to benefit from anxiety-sensitive agents.
A Logic of Only-Believing over Arbitrary Probability Distributions

When it comes to robotic agents operating in an uncertain world, a major concern in knowledge representation is to better relate high-level logical accounts of beliefs and actions to the low-level probabilistic sensorimotor data. Perhaps the most general formalism for dealing with degrees of belief in formulas, and in particular, with how that should evolve in the presence of noisy sensing and acting is the first-order logical account by Bacchus, Halpern, and Levesque. The main advantage of such a logical account is that it allows a specification of beliefs that can be partial or incomplete, in keeping with whatever information is available about the domain, making it particularly attractive for general-purpose cognitive robotics. Recently, this model was extended to handle continuous probability distributions. However, it is limited to finitely many nullary fluents and defines beliefs and integration axiomatically, the latter making semantic proofs about beliefs and meta-beliefs difficult. In this paper, we revisit the continuous model and cast it in a modal language. We will go beyond nullary fluents and allow fluents of arbitrary arity as is usual in the standard situation calculus. This necessitates a new and general treatment of probabilities on possible worlds, where we define measures on uncountably many worlds that interpret infinitely many fluents. We then show how this leads to a fairly simple definition of knowing, degrees of belief, and only-knowing. Properties thereof will also be analyzed. In this paper, we focus on the static setting and conclude with some thoughts about extending this account to actions as the next step and what challenges might arise.
ADAPTIVE DATA-DEPENDENT MATRIX NORM BASED GAUSSIAN KERNEL FOR FACIAL FEATURE EXTRACTION

In this paper, we propose a novel kernel named Adaptive Data-dependent Matrix Norm Based Gaussian Kernel (ADM-Gaussian kernel) for facial feature extrac- tion. As a popular facial feature extraction method for face recognition, the current kernel method endures some problems. Firstly, the face image must be transformed to the vector, which leads to the large storage requirements and the large computational effort, and sec- ondly since the different geometrical structures lead to the different class discrimination of the data in the feature space, the performance of the kernel method is influenced when kernels are inappropriately selected. In order to solve these problems, firstly we create a novel matrix norm based Gaussian kernel which views images as matrices for facial feature extraction, which is the basic kernel for the data-dependent kernel. Secondly we apply a novel maximum margin criterion to seek the adaptive expansion coefficients of the data-dependent kernel, which leads to the largest class discrimination of the data in the feature space. Experiments on ORL and Yale databases demonstrate the effectiveness of the proposed algorithm. Keywords: Kernel method, Gaussian kernel, Matrix norm based Gaussian kernel, Adaptive matrix norm based Gaussian kernel, Data-dependent kernel
How Does Fairness Affect the Complexity of Gerrymandering?

Gerrymandering is a common way to externally manipulate district-based elections where the electorate is (artificially) redistricted with an aim to favour a particular political party to win more districts in the election. Formally, given a set of 𝑚 possible locations of ballot boxes and a set of 𝑛 voters (with known preferences) is it possible to choose 𝑘 specific locations for the ballot boxes so that the desired candidate wins in at least 𝑙 of them? Lewenberg et al. [AAMAS ’17] and Eiben et al. [AAAI ’20] studied the classical and fine-grained complexity (respectively) of the gerrymandering problem.
Explainable Ensemble Classification Model based on Argumentation

An ensemble classifier considers several base classifiers to make its predictions. It is generally seen as a black-box which, in addition, overlooks conflicts that may exist between base classifiers’ rules. This paper proposes two novel ensemble classifiers that bridge the above gaps. They consider 𝑘 base classifiers, each of which is a set of classification rules called theory, and a theory of domain knowledge. They build an argumentation system over the 𝑘 + 1 theories for identifying and solving possible conflicts between classification rules, and use the winning rules for making predictions. We show that the two classifiers guarantee some desirable properties including explainability, compliance to knowledge, and a global compatibility of the rules they use for making predictions.
Deliberation as Evidence Disclosure: A Tale of Two Protocol Types

We study a model inspired by deliberative practice, in which agents selectively disclose evidence about a set of alternatives prior to taking a final decision on them. We are interested in whether such a process, when iterated to termination, results in the objectively best alternatives being selected—thereby lending support to the idea that groups can be wise even when their members communicate with each other. We find that, under certain restrictions on the relative amounts of evidence, together with the actions available to the agents, there exist deliberation protocols in each of the two families we look at (i.e., simultaneous and sequential) that offer desirable guarantees. Simulation results further complement this picture, by showing how the distribution of evidence among the agents influences parameters of interest, such as the outcome of the protocols and the number of rounds until termination.
Improving Cooperative Multi-Agent Exploration via Surprise Minimization and Social Influence Maximization

In multi-agent reinforcement learning (MARL), the uncertainty of state change and the inconsistency between agents’ local observation and global information are always the main obstacles of cooperative multi-agent exploration. To address these challenges, we propose a novel MARL exploration method by combining surprise minimization and social influence maximization. Considering state entropy as a measure of surprise, surprise minimization is achieved by rewarding the individual’s intrinsic motivation (or re-wards) for coping with more stable and familiar situations, hence promoting the policy learning. Furthermore, we introduce mutual information between agents’ actions as a regularizer to maximize the social influence via optimizing a tractable variational estimation. In this way, the agents are guided to interact positively with one another by navigating between states that favor cooperation.
Last-mile Collaboration: A Decentralized Mechanism with Performance Guarantees and its Implementation

The last-mile urban freight market is characterised by soaring fragmentation, fierce competition and low profit margin. Horizontal collaboration could enable operators to exchange customers and coordinate routes, resulting in reduced costs and higher level of service. Previous research has focused on combinatorial auctions and scalable mechanisms are still limited. In this work, we pro-pose an Iterative, Decentralized, and Auction-based Mechanism (IDAM) which is individually rational and budget balanced. It par-allelizes several independent local auctions but still guarantees a bounded performance. When considering its best-case performance, IDAM could be as efficient as the centralized optimization while the worst-case performance depends on the fleet capacity and spatial distribution of customers. A case study of the Inner London Area, involving 50 companies and 1000 customers, showed that our approach achieves up to 76% cost savings.
A Deontic Logic of Knowingly Complying

We introduce a logic for representing the deontic notion of know-ingly complying –associated to an agent’s conciousness of taking a normative course of action for achieving a certain goal. Our logic features an operator for describing normative courses of actions, and another operator for describing what each agent knowingly complies with. We provide a sound and complete axiom system for our logic, and study the computational complexity of its satisfiability problem. Finally, we extend our logic with an additional operator for capturing the general abilities of the agents. This operator enables us to distinguish ‘what agents can do’ and ‘what agents do according to norms’. For this extension, we also provide a sound and complete axiom system.
Altruism, Collectivism and Egalitarianism: On a Variety of Prosocial Behaviors in Binary Networked Public Goods Games

Binary Networked public goods (BNPG) game consists of a network 𝐺 = ( 𝑉,𝐸 ) with n players residing as nodes in a network and making a YES/NO decision to invest a public project. Examples of such public projects include face mask wearing during a pandemic, crime reporting and vaccination, etc. Most of the conventional modes of BNPG games solely posit egoism as the motivation of players: they only care about their own benefits. However, a series of real-world examples show that people have a wide range of prosocial behaviors in making decisions. To address this property, we introduce a novel extension of BNPG games to account for three kinds of prosocial motivations: altruism, collectivism, and egalitarianism. We revise utility functions to reflect different prosocial motivations with respect to the welfare of others, mediated by a prosocial graph. We develop computational complexity results to decide the existence of pure strategy Nash equilibrium in these models, for cases where the prosocial graph is a tree, a clique or a general network. We further discuss the Prosocial Network Modification (PNM) problem, in which a principal can change the network structure within a budget constraint, to induce a given strategy profile with respect to an equilibrium. For all three types of PNM problems, we completely characterize their corresponding computational complexity results.
Merging Control and Feedback to Reduce Mode Confusion in the Cockpit

Mode confusion and automation surprises in aviation raise questions about the design of flight deck interfaces. Prior research investigated the use of the current interfaces and how they can impact the pilot’s awareness of modes, and proposed design solutions to reduce mode confusions by improving feedback and interaction with modes. This paper explores a novel design that brings together mode control and feedback in a single interface. The interface aims to reduce mode confusions. Moreover, the paper highlights 5 key dimensions that influenced the design. In the future, we intend to evaluate the proposed interface to validate its benefits.
Human Body Impact on UWB Antenna Radiation

We experimentally quantify the radiation of a small Ultra-Wideband (UWB) antenna placed on a human body. The measurements are performed for difierent antenna locations on the body, namely the head, torso and belt. First the antenna is measured in free space as reference, and then the in∞uences of the body are investigated for difierent frequencies and antenna polarizations. We observe minimal signal blockage when the antenna is located on the head. It is around 5dB{10dB loss for all polarizations. For the belt and torso, the blockage is 10dB to 35dB, for both polarizations.
How People Think about Automations in Smart Homes

Several tools have been put forward for creating automations in smart homes both at a research and commercial level. However, often people still encounter difficulties in specifying them in order to obtain the desired behaviour. To design authoring tools in which even people without programming experience can flexibly indicate the desired automations it is important to have a better understanding of user needs and how they think and describe the automations. In this contribution, we introduce a user study with such goals and provide an initial discussion of what has emerged.
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO

We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems: participants train teams of agents to complete a multi-task objective against opponents not seen during training. The competition combines relatively complex environment design with large numbers of agents in the environment. The top submissions demonstrate strong success on this task using mostly standard reinforcement learning (RL) methods combined with domain-specific engineering. We summarize the competition design and results and suggest that, as an academic community, competitions may be a powerful approach to solving hard problems and establishing a solid benchmark for algorithms. We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.
Addressing Passenger-Vehicle Conflicts: Challenges and Research Directions

As the complexity and capability of vehicles increase, they adopt higher degrees of responsibility and execute greater levels of autonomous decision-making. On the way to full automation, they act with the user as a team. This can lead to conflicts that are detrimental to automation engagement. We explore factors influencing conflict development with automated vehicles and discuss challenges and possible future research directions.
Modeling Dynamic Environments with Scene Graph Memory

Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link prediction on partially observable dynamic graphs. Our graph is a representation of a scene in which rooms and objects are nodes, and their relationships are encoded in the edges; only parts of the changing graph are known to the agent at each timestep. This partial observability poses a challenge to existing link prediction approaches, which we address. We propose a novel state representation -- Scene Graph Memory (SGM) -- with captures the agent's accumulated set of observations, as well as a neural net architecture called a Node Edge Predictor (NEP) that extracts information from the SGM to search efficiently. We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy. The codebase and more can be found at https://www.scenegraphmemory. com.
Computationally Tractable SLAM

Simultaneous Localisation and Mapping (SLAM) is a well-known, and seemingly well-understood problem in robot navigation. A robot with some kind of sensing device moves through an environment making measurements of features or objects in that space. As it does so it creates and maintains a map of the locations of these features, and of its own location in the map. SLAM usually refers to navigation techniques involving the use of the Kalman lter or one of its variants. Using this lter we gain a very clear understanding of the map update process, but at a price | the computational requirements for the optimal update, however cleverly the equations are rearranged and calculated, increase at least at a rate proportional to the square of the number of features in the map. The only solution is to limit the range of the robot, or use a suboptimal method. A number of possible short-cut methods have been proposed. The report describes many of the methods in the current literature and some new ones which have been tested on the Oxford system. This report looks at exactly what is meant by whether the method is working, and concludes that not only do few methods give convincing proof of validity, but that such proof is actually impossible in all but the simplest cases, and only sucien t empirical testing can be seen as true evidence. Some ideas about the best way forward are suggested.
Explanation through Dialogue for Reasoning Systems

Explainability and transparency are becoming more critical in logical reasoning, such as in self-driving cars and medical care, where poor decisions can cause harm and, in the worst situations, death. To ensure such systems are morally sound, reliable, and secure, they must be capable of explaining their output or procedures in a human-understandable way. In this research, a dialogue explanation framework for Rule-based reasoning systems is presented to identify and explain discrepancies between the user and the system. It allows the system to explain itself by simply asking and answering “Why? ” and “Why not? ” questions. The formal properties of this framework and a small user evaluation that contrasts dialogue-based explanations with the proof trees generated by the reasoning system are described.
Relaxed Exploration Constrained Reinforcement Learning

This extended abstract introduces a novel setting of reinforcement learning with constraints, called Relaxed Exploration Constrained Reinforcement Learning (RECRL). As in standard constrained reinforcement learning (CRL), the aim is to find a policy that maximizes environmental return subject to a set of constraints. However, in RECRL there is an initial training phase in which the constraints are relaxed, thus the agent can explore the environment more freely. When training is done, the agent is deployed in the environment and is required to fully satisfy all constraints. As an initial approach to RECRL problems, we introduce a curriculum-based approach, named CLiC, that can be applied to existing CRL algorithms to improve their exploration during the training phase while allowing them to gradually converge to a policy that satisfies the full set of constraints. Empirical evaluation shows that CLiC produces policies with a higher return during deployment than policies learned when training is done using only the strict set of constraints.