JournalTOCs

Autonomous Agents and Multi-Agent Systems
Journal Prestige (SJR): 0.429

Citation Impact (citeScore): 2
Number of Followers: 9

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7454 - ISSN (Online) 1387-2532
Published by Springer-Verlag

[2468 journals]

Efficiently reconfiguring a connected swarm of labeled robots
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  Abstract: Abstract When considering motion planning for a swarm of n labeled robots, we need to rearrange a given start configuration into a desired target configuration via a sequence of parallel, collision-free moves. The objective is to reach the new configuration in a minimum amount of time. Problems of this type have been considered before, with recent notable results achieving constant stretch for parallel reconfiguration: If mapping the start configuration to the target configuration requires a maximum Manhattan distance of d, the total duration of an overall schedule can be bounded to \(\mathcal {O}(d)\) , which is optimal up to constant factors. An important constraint for coordinated reconfiguration is to keep the swarm connected after each time step. In previous work, constant stretch could only be achieved if disconnected reconfiguration is allowed, or for scaled configurations of unlabeled robots; on the other hand, the existence of non-constant lower bounds on the stretch factor was unknown. We resolve these major open problems by (1) establishing a lower bound of \(\Omega (\sqrt{n})\) for connected, labeled reconfiguration and, most importantly, by (2) proving that for scaled arrangements, constant stretch for connected, labeled reconfiguration can be achieved. In addition, we show that (3) it is NP-complete to decide whether a makespan of 2 can be achieved, while it is possible to check in polynomial time whether a schedule of makespan 1 exists.
  PubDate: 2024-08-07
Carbon trading supply chain management based on constrained deep
reinforcement learning
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  Abstract: Abstract The issue of carbon emissions is a critical global concern, and how to effectively reduce energy consumption and emissions is a challenge faced by the industrial sector, which is highly emphasized in supply chain management. The complexity arises from the intricate coupling mechanism between carbon trading and ordering. T he large-scale state space involved and various constraints make cost optimization difficult. Carbon quota constraints and sequential decision-making exacerbate the challenges for businesses. Existing research implements rule-based and heuristic numerical simulation, which struggles to adapt to time-varying environments. We develop a unified framework from the perspective of Constrained Markov Decision Processes (CMDP). Constrained Deep Reinforcement Learning (DRL) with its powerful high-dimensional representations of neural networks and effective decision-making capabilities under constraints, provides a potential solution for supply chain management that includes carbon trading. DRL with constraints is a crucial tool to study cost optimization for enterprises. This paper constructs a DRL algorithm for Double Order based on PPO-Lagrangian (DOPPOL), aimed at addressing a supply chain management model that integrates carbon trading decisions and ordering decisions. The results indicate that businesses can optimize both business and carbon costs, thereby increasing overall profits, as well as adapt to various demand uncertainties. DOPPOL outperforms the traditional method (s, S) in fluctuating demand scenarios. By introducing carbon trading, enterprises are able to adjust supply chain orders and carbon emissions through interaction, and improve operational efficiency. Finally, we emphasize the significant role of carbon pricing in enterprise contracts in terms of profitability, as reasonable prices can help control carbon emissions and reduce costs. Our research is of great importance in achieving climate change control, as well as promoting sustainability.
  PubDate: 2024-08-06
Envy-freeness in 3D hedonic games
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  Abstract: Abstract We study the problem of fairly partitioning a set of agents into coalitions based on the agents’ additively separable preferences, which can also be viewed as a hedonic game. We study three successively weaker solution concepts, related to envy, weakly justified envy, and justified envy. In a model in which coalitions may have any size, trivial solutions exist for these concepts, which provides a strong motivation for placing restrictions on coalition size. In this paper, we require feasible coalitions to have size three. We study the existence of partitions that are envy-free, weakly justified envy-free, and justified envy-free, and the computational complexity of finding such partitions, if they exist. We impose various restrictions on the agents’ preferences and present a complete complexity classification in terms of these restrictions.
  PubDate: 2024-07-30
Team-wise effective communication in multi-agent reinforcement learning
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  Abstract: Abstract Effective communication is crucial for the success of multi-agent systems, as it promotes collaboration for attaining joint objectives and enhances competitive efforts towards individual goals. In the context of multi-agent reinforcement learning, determining “whom”, “how” and “what” to communicate are crucial factors for developing effective policies. Therefore, we propose TeamComm, a novel framework for multi-agent communication reinforcement learning. First, it introduces a dynamic team reasoning policy, allowing agents to dynamically form teams and adapt their communication partners based on task requirements and environment states in cooperative or competitive scenarios. Second, TeamComm utilizes heterogeneous communication channels consisting of intra- and inter-team to achieve diverse information flow. Lastly, TeamComm leverages the information bottleneck principle to optimize communication content, guiding agents to convey relevant and valuable information. Through experimental evaluations on three popular environments with seven different scenarios, we empirically demonstrate the superior performance of TeamComm compared to existing methods.
  PubDate: 2024-07-18
When is it acceptable to break the rules' Knowledge representation of
moral judgements based on empirical data
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  Abstract: Abstract Constraining the actions of AI systems is one promising way to ensure that these systems behave in a way that is morally acceptable to humans. But constraints alone come with drawbacks as in many AI systems, they are not flexible. If these constraints are too rigid, they can preclude actions that are actually acceptable in certain, contextual situations. Humans, on the other hand, can often decide when a simple and seemingly inflexible rule should actually be overridden based on the context. In this paper, we empirically investigate the way humans make these contextual moral judgements, with the goal of building AI systems that understand when to follow and when to override constraints. We propose a novel and general preference-based graphical model that captures a modification of standard dual process theories of moral judgment. We then detail the design, implementation, and results of a study of human participants who judge whether it is acceptable to break a well-established rule: no cutting in line. We then develop an instance of our model and compare its performance to that of standard machine learning approaches on the task of predicting the behavior of human participants in the study, showing that our preference-based approach more accurately captures the judgments of human decision-makers. It also provides a flexible method to model the relationship between variables for moral decision-making tasks that can be generalized to other settings.
  PubDate: 2024-07-13
Emergent cooperation from mutual acknowledgment exchange in multi-agent
reinforcement learning
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  Abstract: Abstract Peer incentivization (PI) is a recent approach where all agents learn to reward or penalize each other in a distributed fashion, which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly incorporated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information, which limits scalability and applicability to real-world scenarios where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to exchange acknowledgment tokens as incentives to shape individual rewards mutually. All agents condition their token transmissions on the locally estimated quality of their own situations based on environmental rewards and received tokens. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can deviate from the protocol and communication failures can occur. We also evaluate the sensitivity of MATE w.r.t. the choice of token values.
  PubDate: 2024-07-11
An agent-based persuasion model using emotion-driven concession and
multi-objective optimization
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  Abstract: Abstract Multi-attribute negotiation is essentially a multi-objective optimization (MOO) problem, where models of agent-based emotional persuasion (EP) can exhibit characteristics of anthropomorphism. This paper proposes a novel EP model by fusing the strategy of emotion-driven concession with the method of multi-objective optimization (EDC-MOO). Firstly, a comprehensive emotion model is designed to enhance the authenticity of the emotion. A novel concession strategy is then proposed to enable the concession to be dynamically tuned by the emotions of the agents. Finally, a new EP model is constructed by integrating emotion, historical transaction, persuasion behavior, and concession strategy under the framework of MOO. Comprehensive experiments on bilateral negotiation are conducted to illustrate and validate the effectiveness of EDC-MOO. These include an analysis of negotiations under five distinct persuasion styles, a comparison of EDC-MOO with a non-emotion-based MOO negotiation model and classic trade-off strategies, negotiations between emotion-driven and non-emotion-driven agents, and negotiations involving human participants. A detailed analysis of parameter sensitivity is also discussed. Experimental results show that the proposed EDC-MOO model can enhance the diversity of the negotiation process and the anthropomorphism of the bilateral agents, thereby improving the social welfare of both parties.
  PubDate: 2024-07-09
From large language models to small logic programs: building global
explanations from disagreeing local post-hoc explainers
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  Abstract: Abstract The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.
  PubDate: 2024-07-08
Navigating in a space of game views
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  Abstract: Abstract Game-theoretic modeling entails selecting the particular elements of a complex strategic situation deemed most salient for strategic analysis. Recognizing that any game model is one of many possible views of the situation, we term this a game view, and propose that sophisticated game reasoning would naturally consider multiple views. We introduce a conceptual framework, game view navigation, for game-theoretic reasoning through a process of constructing and analyzing a series of game views. The approach is illustrated using a variety of existing methods, which can be cast in terms of navigation patterns within this framework. By formally defining these as well as recently introduced ideas as navigating in a space of game views, we recognize common themes and opportunities for generalization. Game view navigation thus provides a unifying perspective that sheds light on connections between disparate reasoning methods, and defines a design space for creation of new techniques. We further apply the framework by defining and exploring new techniques based on modulating player aggregation in equilibrium search.
  PubDate: 2024-07-06
Beyond the echo chamber: modelling open-mindedness in citizens’
assemblies
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  Abstract: Abstract A Citizens’ assembly (CA) is a democratic innovation tool where a randomly selected group of citizens deliberate a topic over multiple rounds to generate, and then vote upon, policy recommendations. Despite growing popularity, little work exists on understanding how CA inputs, such as the expert selection process and the mixing method used for discussion groups, affect results. In this work, we model CA deliberation and opinion change as a multi-agent systems problem. We introduce and formalise a set of criteria for evaluating successful CAs using insight from previous CA trials and theoretical results. Although real-world trials meet these criteria, we show that finding a model that does so is non-trivial; through simulations and theoretical arguments, we show that established opinion change models fail at least one of these criteria. We therefore propose an augmented opinion change model with a latent ‘open-mindedness’ variable, which sufficiently captures people’s propensity to change opinion. We show that data from the CA of Scotland indicates a latent variable both exists and resembles the concept of open-mindedness in the literature. We calibrate parameters against real CA data, demonstrating our model’s ecological validity, before running simulations across a range of realistic global parameters, with each simulation satisfying our criteria. Specifically, simulations meet criteria regardless of expert selection, expert ordering, participant extremism, and sub-optimal participant grouping, which has ramifications for optimised algorithmic approaches in the computational CA space.
  PubDate: 2024-07-03
On preferences and reward policies over rankings
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  Abstract: Abstract We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.
  PubDate: 2024-07-02
Parameterized complexity of candidate nomination for elections based on
positional scoring rules
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  Abstract: Abstract Consider elections where the set of candidates is partitioned into parties, and each party must nominate exactly one candidate. The Possible President problem asks whether some candidate of a given party can become the unique winner of the election for some nominations from other parties. We perform a multivariate computational complexity analysis of Possible President for several classes of elections based on positional scoring rules. We consider the following parameters: the size of the largest party, the number of parties, the number of voters and the number of voter types. We provide a complete computational map of Possible President in the sense that for each choice of the four possible parameters as (i) constant, (ii) parameter, or (iii) unbounded, we classify the computational complexity of the resulting problem as either polynomial-time solvable or NP-complete, and for parameterized versions as either fixed-parameter tractable or W[1]-hard with respect to the parameters considered.
  PubDate: 2024-07-01
Assimilating human feedback from autonomous vehicle interaction in
reinforcement learning models
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  Abstract: Abstract A significant challenge for real-world automated vehicles (AVs) is their interaction with human pedestrians. This paper develops a methodology to directly elicit the AV behaviour pedestrians find suitable by collecting quantitative data that can be used to measure and improve an algorithm's performance. Starting with a Deep Q Network (DQN) trained on a simple Pygame/Python-based pedestrian crossing environment, the reward structure was adapted to allow adjustment by human feedback. Feedback was collected by eliciting behavioural judgements collected from people in a controlled environment. The reward was shaped by the inter-action vector, decomposed into feature aspects for relevant behaviours, thereby facilitating both implicit preference selection and explicit task discovery in tandem. Using computational RL and behavioural-science techniques, we harness a formal iterative feedback loop where the rewards were repeatedly adapted based on human behavioural judgments. Experiments were conducted with 124 participants that showed strong initial improvement in the judgement of AV behaviours with the adaptive reward structure. The results indicate that the primary avenue for enhancing vehicle behaviour lies in the predictability of its movements when introduced. More broadly, recognising AV behaviours that receive favourable human judgments can pave the way for enhanced performance.
  PubDate: 2024-06-26
A comprehensive analysis of agent factorization and learning algorithms in
multiagent systems
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  Abstract: Abstract In multiagent systems, agent factorization denotes the process of segmenting the state-action space of the environment into distinct components, each corresponding to an individual agent, and subsequently determining the interactions among these agents. Effective agent factorization significantly influences the system performance of real-world industrial applications. In this work, we try to assess the performance impact of agent factorization when using different learning algorithms in multiagent coordination settings; and thus discover the source of performance quality of the multiagent solution derived by combining different factorizations with different learning algorithms. To this end, we evaluate twelve different agent factorization instances—or agent definitions—in the warehouse traffic management domain, comparing the training performance of (primarily) three learning algorithms suitable for learning coordinated multiagent policies: the Evolutionary Strategies (ES), the Canonical Evolutionary Strategies (CES), and a genetic algorithm (CCEA) previously used in a similar setting. Our results demonstrate that the performance of different learning algorithms is affected in different ways by alternative agent definitions. Given this, we can conclude that many important multiagent coordination problems can eventually be solved more efficiently by a suitable agent factorization combined with an appropriate choice of a learning algorithm. Moreover, our work shows that ES and CES are effective learning algorithms for the warehouse traffic management domain, while, interestingly, celebrated policy gradient methods do not fare well in this complex real-world problem setting. As such, our work offers insights into the intrinsic properties of the learning algorithms that make them well-suited for this problem domain. More broadly, our work demonstrates the need to identify appropriate agent definitions-multiagent learning algorithm pairings in order to solve specific complex problems effectively, and provides insights into the general characteristics that such pairings must possess to address broad classes of multiagent learning and coordination problems.
  PubDate: 2024-06-26
Correction: Warmth and competence in human-agent cooperation
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  PubDate: 2024-06-07
  DOI: 10.1007/s10458-024-09654-9
A framework for trust-related knowledge transfer in human–robot
interaction
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  Abstract: Abstract Trustworthy human–robot interaction (HRI) during activities of daily living (ADL) presents an interesting and challenging domain for assistive robots, particularly since methods for estimating the trust level of a human participant towards the assistive robot are still in their infancy. Trust is a multifaced concept which is affected by the interactions between the robot and the human, and depends, among other factors, on the history of the robot’s functionality, the task and the environmental state. In this paper, we are concerned with the challenge of trust transfer, i.e. whether experiences from interactions on a previous collaborative task can be taken into consideration in the trust level inference for a new collaborative task. This has the potential of avoiding re-computing trust levels from scratch for every new situation. The key challenge here is to automatically evaluate the similarity between the original and the novel situation, then adapt the robot’s behaviour to the novel situation using previous experience with various objects and tasks. To achieve this, we measure the semantic similarity between concepts in knowledge graphs (KGs) and adapt the robot’s actions towards a specific user based on personalised interaction histories. These actions are grounded and then verified before execution using a geometric motion planner to generate feasible trajectories in novel situations. This framework has been experimentally tested in human–robot handover tasks in different kitchen scene contexts. We conclude that trust-related knowledge positively influences and improves collaboration in both performance and time aspects.
  PubDate: 2024-05-29
  DOI: 10.1007/s10458-024-09653-w
Warmth and competence in human-agent cooperation
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  Abstract: Abstract Interaction and cooperation with humans are overarching aspirations of artificial intelligence research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through “objective” metrics such as task performance, obscuring potential variation in the levels of trust and subjective preference that different agents garner. To better understand the factors shaping subjective preferences in human-agent cooperation, we train deep reinforcement learning agents in Coins, a two-player social dilemma. We recruit \(N = 501\) participants for a human-agent cooperation study and measure their impressions of the agents they encounter. Participants’ perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics. Drawing inspiration from social science and biology research, we subsequently implement a new “partner choice” framework to elicit revealed preferences: after playing an episode with an agent, participants are asked whether they would like to play the next episode with the same agent or to play alone. As with stated preferences, social perception better predicts participants’ revealed preferences than does objective performance. Given these results, we recommend human-agent interaction researchers routinely incorporate the measurement of social perception and subjective preferences into their studies.
  PubDate: 2024-05-22
  DOI: 10.1007/s10458-024-09649-6
Majority opinion diffusion: when tie-breaking rule matters
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  Abstract: Abstract Consider a graph G, which represents a social network, and assume that initially each node is either blue or white (corresponding to its opinion on a certain topic). In each round, all nodes simultaneously update their color to the most frequent color in their neighborhood. This is called the Majority Model (MM) if a node keeps its color in case of a tie and the Random Majority Model (RMM) if it chooses blue with probability 1/2 and white otherwise. We study the convergence properties of the above models, including stabilization time, periodicity, and the number of stable configurations. In particular, we prove that the stabilization time in RMM can be exponential in the size of the graph, which is in contrast with the previously known polynomial bound on the stabilization time of MM. We provide some bounds on the minimum size of a winning set, which is a set of nodes whose agreement on a color in the initial coloring enforces the process to end in a coloring where all nodes share that color. Furthermore, we calculate the expected final number of blue nodes for a random initial coloring, where each node is colored blue independently with some fixed probability, on cycle graphs. Finally, we conduct some experiments which complement our theoretical findings and also let us investigate other aspects of the models.
  PubDate: 2024-05-20
  DOI: 10.1007/s10458-024-09651-y
Tackling school segregation with transportation network interventions: an
agent-based modelling approach
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  Abstract: Abstract We address the emerging challenge of school segregation within the context of free school choice systems. Households take into account both proximity and demographic composition when deciding on which schools to send their children to, potentially exacerbating residential segregation. This raises an important question: can we strategically intervene in transportation networks to enhance school access and mitigate segregation' In this paper, we propose a novel, network agent-based model to explore this question. Through simulations in both synthetic and real-world networks, we demonstrate that enhancing school accessibility via transportation network interventions can lead to a reduction in school segregation, under specific conditions. We introduce group-based network centrality measures and show that increasing the centrality of certain neighborhood nodes with respect to a transportation network can be an effective strategy for strategic interventions. We conduct experiments in two synthetic network environments, as well as in an environment based on real-world data from Amsterdam, the Netherlands. In both cases, we simulate a population of representative agents emulating real citizens’ schooling preferences, and we assume that agents belong to two different groups (e.g., based on migration background). We show that, under specific homophily regimes in the population, school segregation can be reduced by up to 35%. Our proposed framework provides the foundation to explore how citizens’ preferences, school capacity, and public transportation can shape patterns of urban segregation.
  PubDate: 2024-05-20
  DOI: 10.1007/s10458-024-09652-x
Logic-based cognitive planning for conversational agents
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  Abstract: Abstract This paper presents a novel approach to cognitive planning based on an NP-complete logic of explicit and implicit belief whose satisfiability checking problem is reduced to SAT. We illustrate the potential for application of our model by formalizing and then implementing a human–machine interaction scenario in which an artificial agent interacts with a human agent through dialogue and tries to motivate her to practice a sport. To make persuasion effective, the artificial agent needs a model of the human’s beliefs and desires which is built during interaction through a sequence of belief revision operations. We consider two cognitive planning algorithms and compare their performances, a brute force algorithm based on SAT and a QBF-based algorithm.
  PubDate: 2024-05-20
  DOI: 10.1007/s10458-024-09646-9

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