Belief-Desire-Intention Research Articles

SONAR: An Adaptive Control Architecture for Social Norm Aware Robots

AbstractRecent advances in robotics and artificial intelligence have made it necessary or desired for humans to get involved in interactions with social robots. A key factor for the human acceptance of these robots is their awareness of environmental and social norms. In this paper, we introduce SONAR (for SOcial Norm Aware Robots), a novel robot-agnostic control architecture aimed at enabling social agents to autonomously recognize, act upon, and learn over time social norms during interactions with humans. SONAR integrates several state-of-the-art theories and technologies, including the belief-desire-intention (BDI) model of reasoning and decision making for rational agents, fuzzy logic theory, and large language models, to support adaptive and norm-aware autonomous decision making. We demonstrate the feasibility and applicability of SONAR via real-life experiments involving human-robot interactions (HRI) using a Nao robot for scenarios of casual conversations between the robot and each participant. The results of our experiments show that our SONAR implementation can effectively and efficiently be used in HRI to provide the robot with environmental and social and norm awareness. Compared to a robot with no explicit social and norm awareness, introducing social and norm awareness via SONAR results in interactions that are perceived as more positive and enjoyable by humans, as well as in higher perceived trust in the social robot. Moreover, we investigate, via computer-based simulations, the extent to which SONAR can be used to learn and adapt to the social norms of different societies. The results of these simulations illustrate that SONAR can successfully learn adequate behaviors in a society from a relatively small amount of data. We publicly release the source code of SONAR, along with data and experiments logs.

Effects of the Human Presence among Robots in the ARIAC 2023 Industrial Automation Competition

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems.

Investigating consumers’ intention to use drone food delivery services: Do personality traits matter?

PurposeThe drone food delivery service (DFDS) is a revolutionary technology owing to its ability to reduce delivery costs, alleviate traffic congestion, and offer a more environmentally friendly alternative compared to traditional delivery methods. While studies have been conducted to understand consumers’ intentions towards DFDS, very few of them considered internal factors such as personality traits. Considering the aforementioned factors, this research employs the belief-desire-intention (BDI) model and the big five personalities (BFP) traits to explore how personality traits (neuroticism, extraversion, agreeableness, conscientiousness, and openness) moderate the associations between the desire for DFDS and consumers’ intention to use DFDS (ITU).Design/methodology/approachSurvey questionnaires were distributed online via Facebook and WhatsApp. A total of 359 responses were considered valid, and the study employed the partial least squares structural equation modelling approach to test the hypotheses.FindingsThe study revealed a positive impact of the desire for DFDS on ITU. Additionally, extraversion and agreeableness were found to moderate the effect of the desire for DFDS on ITU.Originality/valueThis study is the first in DFDS literature to employ the BDI model, showcasing its effective application in understanding the desire-to-intention relationship. Also, this is the first study that explores the moderating role of the BFP traits in the context of DFDS usage intention. The results provide insights for developing marketing strategies to encourage DFDS usage intention based on consumers' personality traits.

An agent architecture for expressive spatial knowledge and reasoning in land use modeling and simulations

This paper presents a new cognitive agent design approach integrating spatial knowledge representation and reasoning in agent-based modeling dedicated to land use simulations. A deep motivation for our agent-centric contribution is the ever-increasing development of spatially explicit agent simulation platforms. We build on this technological evolution and topology theory to endow the agent with human’s spatial representation and reasoning following a Belief–Desire–Intention architecture. A pilot implementation of the methodology with simulation experiments on a hunting model was carried out in GAMA platform to assess agent performances. Simulations display a consistent trend of animal population dynamics and also confirm a high model sensitivity to the integration of spatial knowledge and reasoning in agent-based models of human actor. These results demonstrate a successful implementation and the importance of spatial dimension in the expressive power and the validity of agent-based models. Future research efforts should therefore emphasize on designing human knowledge representation and incorporating learning abilities to improve models efficiency.

Intelligent multi-agent model for energy-efficient communication in wireless sensor networks

The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

On the impact of fuzzy-logic based BDI agent model for cyber–physical systems

Cyber–Physical Systems (CPS) interconnect embedded computing technologies into the physical world, forming a complex, multi-disciplinary, physically-effective system. However, interacting with the physical world brings unpredictability, as real-world events are uncertain and dynamic by its nature. Consequently, CPS must be capable of reasoning about these unpredictable situations and adapt their behaviour accordingly. Therefore, it is essential to enhance the intelligence of CPS to overcome this challenge effectively, leading to the development of smart CPS. As an approach, a reasoning mechanism can support the system to reason about its state and actions to handle unpredictable changes. In this study, we employ intelligent Belief-Desire-Intention (BDI) agents to achieve this objective. Nevertheless, traditional logic approaches based on crisp numbers, which are used in typical BDI agents, may not effectively handle uncertainty. Hence, the reasoning mechanism can be extended with fuzzy logic to deal with run-time uncertainties. Thus, the target system’s suitable inputs, outputs and reasoning phases are fuzzified to indicate the impact of the fuzzy logic on the CPS. To this end, an architecture is provided to deploy BDI agents integrated with a fuzzy reasoning model for handling imprecise information and improving process control. The approach is validated and evaluated on a complex case study with a heterogeneous structure controlled by multiple agents, namely the “smart production system”. The multiple modular and end-to-end experiments conducted reveal that, on the whole, the fuzzy BDI agent outperforms the classical one by up to three times, with only requiring approximately 10% more computation time.

A model for automatic selection of IoT services in ambient assisted living for the elderly

Context:Engineering Ambient Assisted Living applications for the elderly is challenging due to the diversity and rapid changes of both end users’ needs and technological environment equipment. Objective:Assistive applications can be provided as combinations of functionalities provided by IoT devices. With the pervasive availability of functionally equivalent IoT devices, they should be selected according to the specific deployment context in terms of user needs and conditions, device availability, and regulations when the operative context dynamic conditions can be set. Such selection is the objective of this work. Methods:We rely on a conceptual framework for self-adaptation as the enabler for a run-time decision-making process. It allows for representing relations among IoT devices, the functionalities they deliver, and the different modalities these functionalities are provided with in terms of goals, devices, and norms. The framework is based on three fundamental principles: (1) high-level abstractions separating the expected functionality, how it can be delivered, and who is responsible for its delivery; (2) AAL applications as the run-time composition of atomic functionalities; (3) centrality of the user in the system. Result:The Device-Goal-Norm framework is proposed to specify diagrams for different AAL applications, together with the semantics to transform these diagrams into run-time models. We also provide a running implementation of a run-time model based on the belief–desire–intention paradigm.

Quantitative modelling and analysis of BDI agents

Belief–desire–intention (BDI) agents are a popular agent architecture. We extend conceptual agent notation (Can)—a BDI programming language with advanced features such as failure recovery and declarative goals—to include probabilistic action outcomes, e.g. to reflect failed actuators, and probabilistic policies, e.g. for probabilistic plan and intention selection. The extension is encoded in Milner’s bigraphs. Through application of our BigraphER tool and the PRISM model checker, the probability of success (intention completion) under different probabilistic outcomes and plan/event/intention selection strategies can be investigated and compared. We present a smart manufacturing use case. A significant result is that plan selection has limited effect compared with intention selection. We also see that the impact of action failures can be marginal—even when failure probabilities are large—due to the agent making smarter choices.

UAV-based emergency treatment plan for flood disasters at the Hongyanhe nuclear power plant

This paper explores the potential of the joint application of unmanned aerial vehicle(UAV)-based the belief-desire-intention (BDI) algorithm constructed for rescue by NetLogo and the flood model of the Hongyanhe Nuclear Power Plant based on the Global Surface Water (GSW) dataset. The study revealed the high potential of the GSW dataset provides useful data for detecting the occurrence of flood events about the Hongyanhe Nuclear Power Plant, the major features are as: the areas affected by the floods in the grid system for emergency treatment at the nuclear power station were: grid area718, 758, 759, 798, 799, and 839;the most affected grid areas were grid areas 758–759 and 798–799; these were the important areas, incorporating the storage area for wastewater, waste liquid and waste solids, the comprehensive office building, and No. 6 shelter. The study also reveals that the success rate of UAV rescue using a single drone was 59.8% (standard deviation 0.1657, standard error 0.0166, 95% Confidence Interval),and there was no significant difference in the results between the two groups(significance of the one-way analysis of variance P = 0.662 > 0.05); the success rate of UAV rescue using two drones was 58.8% (standard deviation 0.1572, standard error 0.0157, 95% Confidence Interval); the simulated time of completing the rescue task using a single drone was 5484.09 (standard deviation 2447.7519, standard error 244.7752, 95% Confidence Interval); and the simulated time of completing the rescue task using two drones was 4679.22 (standard deviation 3109.4676, standard error 310.9468, 95% Confidence Interval). Thus, the rescue efficiency was higher using two drones than using one drone (level of significance from the one-way analysis of variance P = 0.043 < 0.05).

Rational software agents with the BDI reasoning model for Cyber–Physical Systems

Cyber–Physical Systems (CPSs) are complex and heterogeneous systems. Interacting with the physical world makes CPS unpredictable because of unanticipated events. Therefore, a CPS needs to reason these events autonomously. Henceforward, suitable reasoning mechanisms, such as rational agents with deliberative capabilities, should be selected and integrated into the CPSs. In this way, the integrated multi-agents can reason on environmental changes to find a plan that sustains the system’s operation for CPS. In addition, a layered architecture can pave the way to integrate the rational agents on embedded hardware and control the CPS. To this end, this paper presents an architecture, discusses a reference implementation and elaborates on the high-level integration of agents and CPS. Moreover, a complex and heterogeneous case study is provided to validate the effectiveness of rational agents in conducted experiments. Firstly, the rational agents utilising the belief–desire–intention (BDI) model required approximately three times less development time than simple-reflex agents. Secondly, the proposed approach resulted in up to 3 times less description complexity in language expressiveness. Lastly, the product quality is improved up to 66% by the rational agents and BDI model. As a result, our approach is beneficial to designing multi-agent CPS where it is aimed to use low-level control and high-level reasoning in a single platform.

Boosting stakeholders' intention to participate in socially responsible collective action in megaprojects: perspectives of mega water transfer projects in China

PurposeMegaprojects have large impact on the environment and stakeholders should take collective action to ensure that these projects are developed in a socially responsible manner. Hitherto, it is not known whether group and subjective norms and social identity could compel stakeholders to take socially responsible collective actions in megaprojects. The aim of this study is to design and test a model to boost stakeholders' intention to take socially responsible collective action in the context of mega water transfer projects in China.Design/methodology/approachA quasi-experimental causal research design was adopted to establish cause–effect relationships among the dependent variable (we-intention) and independent variables (subjective norms, group norms, social identity and desire). This study adopts the belief–desire–intention model and social influence theory to empirically investigate how to boost the stakeholders' intention to participate in socially responsible collective action. An online questionnaire survey was conducted and data was collected from 365 respondents who were involved in mega water transfer projects in China. The partial least squares structural equation modeling technique was employed to analyze the data.FindingsThe results from partial least squares analyses indicate that the presence of subjective norms, group norms and social identity (collectively known as social influence process) could increase stakeholders' intention to take socially responsible collective action. In addition, the desire to be socially responsible also boosts stakeholders' intention to take collective action. Desire partially mediates the relationship between social influence process and intention to take socially responsible collective action.Originality/valueThis study adds to existing knowledge by discovering social influence process as an antecedent to taking socially responsible collective action in megaprojects. Strong group norms and subjective norms could propel stakeholders to be more socially responsible. The study also adds to knowledge by discovering that stakeholders' desire to fulfill social responsibility also leads them to take concrete actions. Implications and recommendations are provided on how to manipulate different types of social influence processes to facilitate stakeholders to adopt socially responsible collective action in the process of managing megaprojects.

Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE

Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper.

Scalable BDI-based Multi-Agent System for Digital Design Reviews

The increasing complexity in product development and the lack of knowledge exchange, for example, between development and manufacturing lead to unnecessary iteration loops and high costs. To overcome this situation, an option is the execution of a design review using multi-agent systems to provide designers with a digital assistance system for checking their CAD (computer-aided design) models regarding manufacturability using a milling process. This paper explores how to increase the scalability and robustness of multi-agent systems (MAS) for manufacturability assessment by using runtime-generated BDI (belief-desire-intention) agents and graph-based feature recognition. The applicability and validation of the presented approach is carried out by evaluating different milled part designs.

MoTrans-BDI: Leveraging the Beliefs-Desires-Intentions agent architecture for collaborative model transformation by example

Due to the growing interest in model-driven system development techniques, the efficient design of automated model transformations between heterogeneous models has become a major challenge in software development. While a number of specialized languages have been proposed, aiming at specifying model transformations, there is currently no matured foundation for specifying transformations between such models that are based on designers/experts collaboration in order to propose a solution that satisfies users’ requirements and application constraints. This transformation process is a complex task and must emulate how designers and experts with different perspectives behave and reflect about model transformations. In this paper, we propose a framework based on a novel approach for the specification and design of model transformations called MoTrans-BDI, which leverages Evolutionary Multi-Agent System (EMAS) to simulate designers’ expertise for the transformation of models. Our approach is based on the Belief-Desire-Intention (BDI) agent model and the Contract Net Protocol where agents’ beliefs feed from a series of transformation examples. The emphasis of using the specific model is the opportunity to produce a target model that may be composed of parts from different experts’ designs. We first experimentally evaluate MoTrans-BDI on twelve handmade UML2REL model transformation problems. All types of agents are able to produce perfect target models compared to human experts’ collaboratively-produced target models. Second, we empirically evaluate MoTrans-BDI’s proposal in terms of response time on Star Schema Benchmark (SSB) queries. An application of MoTrans-BDI in the case of UML2REL transformation is performed to stress and highlight the transformation process steps.

XPlaM: A toolkit for automating the acquisition of BDI agent-based Digital Twins of organizations

A Digital Twin ideally manifests the same behaviour (in silico) of its physical counterpart. While considerable attention has been paid to the development of Digital Twins for physical devices/systems, the question of developing Twins for organizations has received relatively little attention. The setting in which we address this problem is very general and, consequently, very challenging. We look at the automatic acquisition of Digital Twins of organizations. To that end, this paper builds on the following two premises: (1) that Digital Twins of organizations can provide value, and (2) that Belief-Desire-Intention (BDI) agents are a particularly effective means for representing Digital Twins. The overall approach is to leverage the externally observable behaviour of the target system and then generate candidate BDI agent programs that best explain (in the sense of formal abduction) the observed behaviour. The candidate agent programs are generated by searching through potentially large hypotheses spaces for possible plans, selection functions and beliefs. The resulting approach suggests that using abduction to generate Digital Twins of organizations in the form of BDI agents can be effective.

Modularization in Belief-Desire-Intention agent programming and artifact-based environments.

This article proposes an extension for the Agents and Artifacts meta-model to enable modularization. We adopt the Belief-Desire-Intention (BDI) model of agency to represent independent and reusable units of code by means of modules. The key idea behind our proposal is to take advantage of the syntactic notion of namespace, i.e., a unique symbol identifier to organize a set of programming elements. On this basis, agents can decide in BDI terms which beliefs, goals, events, percepts and actions will be independently handled by a particular module. The practical feasibility of this approach is demonstrated by developing an auction scenario, where source code enhances scores of coupling, cohesion and complexity metrics, when compared against a non-modular version of the scenario. Our solution allows to address the name-collision issue, provides a use interface for modules that follows the information hiding principle, and promotes software engineering principles related to modularization such as reusability, extensibility and maintainability. Differently from others, our solution allows to encapsulate environment components into modules as it remains independent from a particular BDI agent-oriented programming language.

Features of Decision-Making in the Process of lending to small businesses: the bDi Model and trust indices

The aim of the study is to identify the problems hindering the effective interaction of banks with SMEs and to determine the role of trust and other institutional factors in decision-making. Despite the recovery in the SME lending sector in 2019–2020, the needs of small firms in loans are not fully satisfied. The task of enhancing interactions between banks and the sectors under study requires a fundamental approach and is relevant.The BDI (Belief-DesireIntention) model developed by M. Bratman was used as a method for investigating the decision-making process in the subject area under consideration. The place of this tool in the system of methods of analysis and support of decisionmaking processes is shown; the universality of the BDI model is substantiated and the main areas of its application are indicated.The scientific novelty of the paper is the modification of the BDI model, which is carried out by taking into account the trust factor and increasing the number of decision-making phases.The prospects of using the modified BDI model as a methodological basis for the formation of the architecture of banking ecosystems are considered. On the example of the experience of a quantitative assessment of the trust factor between banks and small firms, the possibilities of using the index method to measure other institutional factors of the modified M. Bratman model and determine the level of feasibility of a transaction planned in the area of lending are investigated.The author concluded that a necessary condition for enhancing the lending activities of banks interacting with small firms is the formation of an adequate institutional environment in accordance with the methodological principles outlined in this article.

An Argumentation-Based Approach for Goal Reasoning and Explanations Generation

Abstract Explainable Artificial Intelligence systems, including intelligent agents, are expected to explain their internal decisions, behaviors and reasoning that produce their choices to the humans (or to other systems) with which they interact. Given this context, the aim of this article is to introduce a practical reasoning agent framework that supports generation of explanations about the goals the agent committed to. Firstly, we present an argumentation-based formalization for supporting goal reasoning. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more granular and refined model than the Beliefs–Desires–Intentions model. We focus on the dynamics of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use formal argumentation reasoning to support the passage of the goals from their initial state until their final state. Secondly, in order that agents based on the proposed formalization be able to generate explanations about the goals they decided to commit to, we endow them with a mechanism for generating both complete and partial explanations. Finally, we use a scenario of rescue robots in order to illustrate the performance of our proposal, for which a simulator was developed to support the agents goal reasoning.

Ethical decision intention of first-line managers: Case study in Malaysia hotel industry

In this study, the researchers try to identify the factors that encourage/prohibit them to reason and behave ethically in order to derive a prescriptive guidance that can enhance the process, while practicing managers to develop policies and programs that strengthened employees’ ethical resolve on the job. In this study, Bratman’ Belief-Desire-Intention (BDI) model used to explain the research framework, the ethical decision intention component (ethical awareness, ethical judgment and ethical intention) are examined how their relationship towards each other. The researchers has employed survey-based approach to meet the study’s objectives and self-administrated questionnaires were used to gather data from 120 first line managers who work in 4-star and 5-star hotels in Malaysia respectively. These significance findings make a unique contribution towards the development of moral theories, as the researchers will not perceive them as individual but a comprehensive process to aware, evaluate and intend to act morally right. Through the findings of this study, it urges organizations to emphasize the importance of the ethical dilemma in their professional education at both the initial stage and through ongoing professional development.

Situation aware intelligent reasoning during disaster situation in smart cities.

Investigating prior methodologies, it has come to our knowledge that in smart cities, a disaster management system needs an autonomous reasoning mechanism to efficiently enhance the situation awareness of disaster sites and reduce its after-effects. Disasters are unavoidable events that occur at anytime and anywhere. Timely response to hazardous situations can save countless lives. Therefore, this paper introduces a multi-agent system (MAS) with a situation-awareness method utilizing NB-IoT, cyan industrial Internet of things (IIOT), and edge intelligence to have efficient energy, optimistic planning, range flexibility, and handle the situation promptly. We introduce the belief-desire-intention (BDI) reasoning mechanism in a MAS to enhance the ability to have disaster information when an event occurs and perform an intelligent reasoning mechanism to act efficiently in a dynamic environment. Moreover, we illustrate the framework using a case study to determine the working of the proposed system. We develop ontology and a prototype model to demonstrate the scalability of our proposed system.