Belief-Desire-Intention Research Articles

Toward Campus Mail Delivery Using BDI

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system.

Proposition of a Distributed Voronoi Partitioning Approach Enhanced with a Dispersion Phase for a Multirobot System

This paper is an extension to our work presented in Ben Slimane and Tagina (in: Nguyen, Pimenidis, Khan and Trawinski (eds) Computational collective intelligence, Springer, Cham, 2018). It deals with the the problem of partitioning the space in an even way between a number of autonomous mobile robots. In our previous work we proposed a distributed one-phased partitioning method where each robot constructs its corresponding Voronoi cell from the information received from its neighbors. We propose in what follows a two-phased partitioning approach, starting with a dispersion task, followed by the distributed Voronoi partitioning as for the one-phased method. For the dispersion phase, we propose a novel parametrized algorithm from which we seek to control the dispersion behavior of the robots. The individual actions of the agents are controlled by the belief–desire–intention model which endows them with the required know-how needed to operate deliberately and readjust the plans dynamically on the go. We show in this paper, through a series of experiments, the results of the dispersion method and the impact of its parameters on the generated maps. We also compare the results of the two partitioning methods to show the impact of the dispersion on the partitioning in terms of the actual performed steps towards convergence and the generated maps for both methods.

Mobile Apps as Personal Assistant Agents: the JaCa-Android Framework for programming Agents-based applications on mobile devices

A relevant application domain for agent-based software is given by mobile and wearable applications. In this context, the impressive progress of technologies in the last decade makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures, such as the Belief–Desire–Intention (BDI) one. Accordingly, in this paper we provide a comprehensive description of the JaCa-Android approach, a framework based on the JaCaMo platform that allows for designing and programming smart mobile apps using cognitive agents based on the BDI architecture and the Agents & Artifacts environment conceptual model. In these years, the framework has been applied in real-world projects and application domains, and extended and evolved accordingly. The aim of the paper is to report our experience about designing and programming mobile apps as personal assistant agents, as well as to discuss in detail the architecture of the framework.

BASTA: BDI-based architecture of simulated traffic agents

ABSTRACT It is expected that connected and autonomous vehicles (CAVs) will soon become a regular means of transportation. To fully leverage the potential of this new technology, it is necessary to equip such cars with efficient algorithms permitting them to drive in a safe and optimal manner. Thereby we aim to design and implement tools for convenient evaluation of strategies for driving and interactions in various settings. In this paper, we present the results of the first stage of our bigger research program on a simulation framework of CAVs. A search for a balance between complexity and comprehensibility of the solution led us to the field of multiagent systems. Beliefs-Desires-Intentions (BDI) systems offer useful abstractions for activities of a single self-driving car and collective intelligence of such vehicles. Indeed, the BDI framework helps to combine two distinct natures of a self-driving car: its reactiveness and proactiveness. Moreover, the modularity of the resulting architectures for an individual CAV and urban traffic induced by these cars makes the design intelligible and flexible. Our prototype verifies the feasibility of this concept.

Distributed Artificial Intelligence Solution for D2D Communication in 5G Networks

Device to Device (D2D) Communication is one of the technology components of the evolving 5G architecture, as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. The above noted improvements in network performance spearheaded a vast amount of research in D2D, which have identified significant challenges that need to be addressed before realizing their full potential in emerging 5G Networks. Towards this end, this paper proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief-Desire-Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the Base Station. The paper includes detailed algorithmic description for the decision of transmission mode, which maximizes the data rate, minimizes the power consumptions, while taking into consideration the computational load. Simulations show the applicability of BDI agents in jointly solving D2D challenges.

The Study for Public Management Policy Utility Evaluation and Optimization System under the Framework of Social Computing Perspective

In recent years, in order to rationalize the allocation of social resources and optimize the implementation of public management policies, scholars have conducted in-depth researches on policy effectiveness. However, at present, most of the study is still at the level of using macrolevel qualitative analysis, and lack of quantitative analysis and evaluation system for the effectiveness of policy implementation. The goal of this article is to discuss the utility evaluation system of public management policy from the perspective of social computing. First, based on the data obtained through questionnaire survey, we obtain indicators of the survey data by using factor analysis, and a new BDI (belief–desire–intention) model is created based on the observation indicators, and then the simulation platform is constructed; then, a brand new quantitative analysis method for policy optimization is proposed by using modified logistic functions as a tool. As application, we conducted the case study for the “Targeted poverty alleviation policy in Yulin region” (Guangxi, China), in which the key indicators for the poverty were established, and then the policy optimization suggestions were given based on the results of simulation experiments. This case study has Chinese characteristics, which might be applied to the poverty alleviation work globally.

An Ontology-Driven IoT based Healthcare Formalism

The recent developments in the Internet of Things (IoT) paradigms have significantly influenced human life, which made their lives much more comfortable, secure and relaxed. With the remarkable upsurge of the smart systems and applications, people are becoming addicted to using these devices and having many dependencies on them. With the advent of modern smart healthcare systems, and significant advancements in IoT enabled technologies have facilitated patients and physicians to be connected in real-time for providing healthcare services whenever and wherever needed. These systems often consist of tiny sensors and usually run on smart devices using mobile applications. However, these systems become even more challenging when there is a need to take intelligent decision making dynamically in a highly decentralized environment. In this paper, we propose a Belief-Desire-Intention (BDI) based multi-agent formalism for ontology-driven healthcare systems that perform BDI based reasoning to take intelligent decision making dynamically in order to achieve the desired goals. We illustrate the use of the proposed approach using a simple case study with the prototypal implementation of heart monitoring applications.

Dealing with Failures for Execution Consistency in Context-aware Systems

In this paper, we consider a symbolic mechanism designed to help guide the performance of actions of a Belief-Desire-Intention (BDI) agent under both concurrency and relevance criteria. Behind the planning activity which can optimistically estimate the relevance of traces, we propose to directly supervise the best effort execution of the intentions as to keep track of the most relevant ones in case of failures. To support the presented method, a typical use case is given that targets the automated guidance of an autonomous vehicle.

A Case Study of Adding Proactivity in Indoor Social Robots Using Belief-Desire-Intention (BDI) Model.

The rise of robots and robotics has proved to be a benefaction to humankind in different aspects. Robotics evolved from a simple button, has seen massive development over the years. Consequently, it has become an integral part of human life as robots are used for a wide range of applications ranging from indoor uses to interplanetary missions. Recently, the use of social robots, in commercial indoor spaces to offer help or social interaction with people, has been quite popular. As such, taking the increasing use of social robots into consideration, many works have been carried out to develop the robots to make them capable of acting like humans. The notion behind this development is the need for robots to offer services without being asked. Social robots should think more like humans and suggest possible and suitable actions by analyzing the environment where they are. Belief–desire–intention (BDI) is one of the most popular models for developing rational agents based on how humans act based on the information derived from an environment. As such, this work defines a foundation architecture to integrate a BDI framework into a social robot to add “act like a human” feature for proactive behaviors. The work validates the proposed architecture by developing a vision-based proactive action using the PROFETA BDI framework in an indoor social robot, Waldo, operated by the robot operating system (ROS).

Modeling Agent-Oriented Methodologies for Landslide Management

One of the most fatal natural disasters in Batu City was a landslide. The percentage of casualties if directly affected by landslides is 47%. This number is quite large when compared to other natural disasters. Even though the potential of the tsunami is the biggest, when compared to the intensity of the occurrence, landslides are the most common and most often cause fatalities. One of the causes of many fatalities in natural disasters is the lack of preparedness management. For that reason the need to develop a technology that can support to reduce fatalities in landslides is needed. One of the technologies used to prevent the number of fatalities caused by natural disasters is to use multi-agent system. One of the advantages of an agent is use Belief-Desire-Intention (BDI) models for building the Muiti-Agent Systems (MAS). The specific objective of this study was to model a simulated natural disaster landslide using the Prometheus Methodology. Evaluation is done by generating a model using jackCode and implementing it using the Java Agent Development Framework. The results of this study indicate that a model made using the Prometheus Methodology can be used as a simulation of natural disaster preparedness for landslides.

FASim: A 3D Serious Game for the First Aid Emergency

Background and Aim. This article aims to present FASim (First Aid Simulator), a 3D serious game for the first aid emergency. This game combines in a single framework the healthcare simulations with serious games and the functionalities of the multi agents systems (MAS). This work devoted to the basic steps in the development of our serious game FASim to learn the first aids and the signs of cardiac arrests which are the leading cause of premature death and the major cause of disability in the world. The practice of serious games in healthcare should be based on four main axes; firstly, the interaction between players is the key element of learning; secondly the adaptation of the game to the profiles of the players. So the game must be in relation with their abilities. Thirdly, the awareness and prevention are the important ingredients in healthcare to promoting the collective recognition of problems of giving the first step towards its resolution; finally, we need some intelligence to organize the first three axes to be able to make quick and accurate decisions. Results and Conclusion. The speed of intervention in the case of rapid cardiopulmonary resuscitation (CPR) increases the probability of survival after sudden cardiac arrest is still the key element to ensure success proven by QRISK method, which is a mathematical application to calculate the probability of having a heart attack. The number of staff must be very high preferences as a result we have created the collaboration to save the maximum life possible, and we implement an adaptive approach to improve the usability of the game. FASim focuses on the action of making quick and precise decisions, which must require a practical reasoning provided by the integration of the BDI (Belief Desire Intention) agent in the Non Players Character (NPCs). All our solutions are focus on the agent-based simulation, which is an excellent approach answer to all the criteria of medical emergencies.

A multi agent system for the online container stacking in seaport terminals

With the continuous development of seaports, problems related to the storage of containers in terminals have emerged. Such problems, referred to in the scientific literature as Container Stacking Problems (CSP) consist in determining the exact location of containers in the storage area of a terminal. Several research works have been conducted to develop systems for the management of container storage operations, which are referred to as Container Terminal Operating Systems (CTOS). Unfortunately, existing systems suffer limitations related to distributed control, online stacking strategies efficiency and their ability to handle dangerous containers. In this paper, we suggest a multi-agent approach for the reactive and decentralized control of container stacking in an uncertain and disturbed environment. A Belief-Desire-Intention (BDI) model has been proposed for the development of the different agents constituting the system. A set of knowledge models and learning mechanisms for disturbance and reactive decision making management are suggested and integrated in the system. The suggested system is able to capture, store and reuse knowledge in order to detect disturbances (as those related to resources breakdown), select the most appropriate storage strategy and determine the most suitable container location.

Application of the GORITE BDI Framework to Human-Autonomy Teaming: A Case Study

Human-Autonomy Teaming (HAT) is of growing interest in the military sector, particularly in its application to war gaming using semi-automated computer generated forces (CGF). In these applications, one or more operators manage multiple semi-autonomous game entities. If effective collaboration (teaming) is to occur between operators and entities, then having effective interaction models is essential if the levels of trust and explanatory capability required for military operations are to be delivered. The Situation Awareness-Based Agent Transparency (SAT) Model has been identified as providing a suitable conceptual framework for such models. However, while the SAT model is informed by the Belief-Desire-Intention (BDI) model of agency, to date there has been no implementation of an interaction model at the level of desires and intentions, i.e. goals. In this paper, we propose that GORITE, a novel BDI framework that employs explicit goal representations and a shared data context for goal execution, provides a suitable platform for the development of SAT-enabled agents. The feasibility of this proposition is demonstrated through the development of a simple but representative CGF case study.

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

Building design involves many challenges and requires to take into account the interaction between the building and the users. Different occupant behaviour models implemented with building simulation tools (thermal, air quality, lighting) have been proposed. Among these, models based on the agent approach seem to be the most promising. However, existing models poorly describe human cognition and the social dimension. Moreover, they are often oriented towards a specific use (thermal simulation, waste management) without being transposable to another field, and they require a significant instantiation effort for each new case, making their use difficult. This article proposes an agent-based model called Li-BIM that simulates the behaviour of the occupants in a building and their indoor comfort. Li-BIM model is structured around the numerical modelling of the building –BIM- (with standard exchange format IFC), a high-resolution cognitive model, and the coupling with various physical models. Li-BIM simulates the reactive, deliberative and social behaviour of occupants in residential dwellings based on the Belief–Desire–Intention architecture. This model, thanks its ease of use and flexibility, is an operational and relevant tool to support building design process with a human-centred approach. An application of the model is presented, focusing on energy consumption and the inhabitants’ comfort. In-situ data obtained from the instrumented house that served as case study have been compared with simulation results from Li-BIM and a standard energy simulation software, demonstrating the reliability of the proposed model.

On Proactive, Transparent, and Verifiable Ethical Reasoning for Robots

Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented, it has, in general, been only initial proofs of principle. Here, we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically critical machine reasoning should be proactive, transparent, and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model and supports proactive, transparent, and verifiable ethical reasoning. To do so, the reasoning component of the ethical layer uses our Python-based belief–desire–intention (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach, we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified.

A multi-agent dynamic system for robust multi-face tracking

The paper presents a new architecture framework in the field of expert and intelligent systems which is based on four paradigms: a novel multi-agent dynamic system architecture (MADS), an extended Belief Desire Intention (EBDI) agent community, autonomy-oriented entities (AOEs), and deep learning concepts. The main impact of the proposed framework is a new approach, or even a new way of thinking, which enables integration of the concepts of deep learning, a conventional approach to solving the domain problem, cognitive agents with mental attitudes, and concepts of nature-inspired computing. All these allow the effective use of the framework in the field of intelligent and expert systems. The significance of the framework lies in its flexibility and adaptability based on the formal logical description of EBDI agents, the definition of the behaviour of AOEs, the use of classical modules for domain problem solving, and modules based on deep-learning concepts. We believe that the example of the adaptation of the proposed architecture framework to robust multi-face tracking illustrates the significance of the proposed framework. In this paper, MADS is adapted to the first two stages of a face de-identification pipeline: robust face detection and multi-face tracking. The proposed architecture of MADS has a two-level hierarchical organization. At the first level there is a manager designed as an Extended Belief Desire Intention (mEBDI) agent. The extension of a manager BDI agent consists of a convolutional neural network-based face detector, a set of autonomous-oriented entities for the elimination of false positive face detections, and a trajectory memory. At the second level, there are many tracking agents (trEBDIs) which consist of a basic BDI agent extended with a face tracker based on position and scale correlation filters, a visual appearance memory, and a trajectory memory. The mEBDI and trEBDI agents are defined by the modal logic and are described at the implementation level. The proposed architecture for a robust multi-face tracking system was tested on a subset of YouTube music videos. The qualitative results, as well as the preliminary quantitative results expressed by the standard testing metrics, demonstrate the effective adaptation of the proposed multi-agent dynamic architecture to a robust multi-face tracking system.

Designing emotional BDI agents: good practices and open questions

AbstractIntelligent agents built on the basis of the BDI (belief–desire–intention) architecture are known as BDI agents. Currently, due to the increasing importance given to the affective capacities, they have evolved giving way to the BDI emotional agents. These agents are generally characterized by affective states such as emotions, mood or personality but sometimes also by affective capacities such as empathy or emotional regulation. In the paper, a review of the most relevant proposals to include emotional aspects in the design of BDI agents is presented. Both BDI formalizations and BDI architecture extensions are covered. From the review, common findings and good practices modeling affect, empathy and regulatory capacities in BDI agents, are extracted. In spite of the great advance in the area several, open questions remain and are also discussed in the paper.

Argumentation-based Intention Formation Process

In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the transition of the goals from their initial state until the last one. Our proposal complies with both supporting relation properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The former means that the support happens since the goal is a desire until it becomes an intention, and the latter that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end. In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the transition of the goals from their initial state until the last one. Our proposal complies with both supporting relation properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The former means that the support happens since the goal is a desire until it becomes an intention, and the latter that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end.

Dimensions in programming multi-agent systems

Abstract Research on Multi-Agent Systems (MAS) has led to the development of several models, languages, and technologies for programming not only agents, but also their interaction, the application environment where they are situated, as well as the organization in which they participate. Research on those topics moved from agent-oriented programming towards multi-agent-oriented programming (MAOP). A MAS program is then designed and developed using a structured set of concepts and associated first-class design and programming abstractions that go beyond the concepts normally associated with agents. They include those related to environment, interaction, and organization. JaCaMo is a platform for MAOP built on top of three seamlessly integrated dimensions (i.e. structured sets of concepts and associated execution platforms): for programming belief desire intention (BDI) agents, their artefact-based environments, and their normative organizations. The key purpose of our work on JaCaMo is to support programmers in exploring the synergy between these dimensions, providing a comprehensive programming model, as well as a corresponding platform for developing and running MAS. This paper provides a practical overview of MAOP using JaCaMo. We show how emphasizing one particular dimension leads to different solutions to the same problem, and discuss the issues of each of those solutions.

Specifying Ontologies in a Social Networking Images’ Sample Obtained with a Multiagent System

In the present research, there are described some promising initial results of induced ontologies from vocabularies of labels in Metroflog, which were selected using Multi agent Systems utilizing a model based on suppositions. We describe the utility of the ontology of aspects as a supplement to a system that marks with labelling and we present our model and results. We propose a probabilistic reviewed model, using seed ontologies to induce ontology of aspects, and we describe how the model can integrate inside of the community’s logistics of labelling. An innovation of our research is having been able to improve the characterization of a group of labels associated with different images and how our multi-agent system uses a Belief–Desire-Intention (BDI) architecture is much more suitable to expand our context-based vocabulary and better describe the details fine of each image and what they really represent for a social group.