Belief-Desire-Intention Agent Research Articles

Improving plan execution robustness through capability aware maintenance of plans by BDI agents

In a realistic environment, intentions of belief-desire-intention (BDI) agents may be threatened by exogenous change. Subsequent activity failure may incur debilitative consequences that hinder both recovery and subsequent goal achievement. Capability aware, maintaining plans (CAMP-BDI) embodies BDI agents with capability knowledge, allowing anticipation of threats to activity success and stimulating the proactive, preventative modification of intended plans. We describe resultant agent-level algorithms and supporting architecture, including extension to provide decentralised, distributed maintenance through structured messaging. Our results show superior goal achievement to a reactive equivalent in a stochastic environment, increasing with the likelihood of debilitative failure effects. We suggest CAMP-BDI offers a valuable approach towards robustness, particularly in tandem with reactive recovery methods.

BDI-agent-based quantum-behaved PSO for shipboard power system reconfiguration

This paper presents a BDI (Belief-Desire-Intention)-agent-based Quantum-Behaved Particle Swarm Optimisation (QPSO) reconfiguration method for shipboard zonal power systems. Shipboard zonal power systems are founded on navy ships, and desired to be highly reconfigurable. Since shipboard power system reconfiguration may change its topology, and load priority should be taken into consideration, this makes shipboard reconfiguration into a non-linear distributed optimisation problem. Specially, switches in the shipboard zonal power system are modelled as intelligent BDI agents. This paper uses swarm intelligence to realise BDI-agent reasoning and optimise its reconfiguration objective. To verify the effectiveness of the proposed approach, comparative simulations are conducted on the method without reconfiguration and reconfiguration method based on PSO/QPSO. Simulation results show that the reconfiguration strategy can achieve success in realising shipboard power system service restoration cases which demonstrate the feasibility and the advantages of the proposed BDI-agent-based QPSO reconfiguration method.

BDI-agent-based quantum-behaved PSO for shipboard power system reconfiguration

This paper presents a BDI (Belief-Desire-Intention)-agent-based Quantum-Behaved Particle Swarm Optimisation (QPSO) reconfiguration method for shipboard zonal power systems. Shipboard zonal power systems are founded on navy ships, and desired to be highly reconfigurable. Since shipboard power system reconfiguration may change its topology, and load priority should be taken into consideration, this makes shipboard reconfiguration into a non-linear distributed optimisation problem. Specially, switches in the shipboard zonal power system are modelled as intelligent BDI agents. This paper uses swarm intelligence to realise BDI-agent reasoning and optimise its reconfiguration objective. To verify the effectiveness of the proposed approach, comparative simulations are conducted on the method without reconfiguration and reconfiguration method based on PSO/QPSO. Simulation results show that the reconfiguration strategy can achieve success in realising shipboard power system service restoration cases which demonstrate the feasibility and the advantages of the proposed BDI-agent-based QPSO reconfiguration method.

Towards A Generic Predictive-Based Plan Selection Approach for BDI Agents

Belief-desire-intention (BDI) model is one of the most promising approaches dedicated to develop intentional agents. It provides developers with required robustness to deal with a wide variety of complex systems where flexibility and intelligence behavior of agents are needed. Existing BDI architectures are typically composed of a set of customizable components that are composed in a reasoning cycle. The plan selection process is one of the customizable components that has an important influence on the overall agent performance. It consists of selecting among a set of applicable plans the most appropriate one to achieve a given goal.In this paper we propose a generic predictive-based plan selection approach, that aims to select for a given goal, the applicable plan that has the highest potential of being executed successfully. Based on what we refer to as selection indices, it calculates for each plan a success probability that will be used later by the selection process to select the appropriate plan.

Network of Unmanned Systems Cyber Attacks over National Economy Infrastructures

This paper aims to analyze the resource management of a Network of Unmanned Systems (NUMS - sensor network system), using a BDI (belief–desire–intention) agent algorithm in junction with Power Transitions Theory and Endogenous Economic Growth - AK model. The research covers sections of only advanced national economies, with a high-technology physical capital, depending on the cyber-economic planning, and focuses on future possible wars between the great world powers.

CBDI: Towards an Architecture for Human–Machine Collaboration

Effective human–machine collaboration is a long standing problem within rehabilitation robotics. Machines that affect peer-to-peer interaction hold promise. Agents can serve as the basis for such machines. Belief–desire–intention (BDI) paradigm is used to achieve human-like intelligence. However, the BDI model does not emulate human-like qualities such as coordination or collaborative planning. Therein lies the motivation to extend the basic BDI agent model. cBDI—an extension to the BDI architecture supporting human–machine collaboration is presented. Two different applications for the cBDI agent are highlighted: (1) human–cBDI agent collaboration to achieve block stacking and (2) cBDI-based collaborative navigation for an intelligent wheelchair (IW). Under scenarios such that the block stacking task cannot be completed by either human or agent alone, we show how the cBDI is tuned to collaborate and accomplish the task. The cBDI-based collaborative navigation controller is for affecting assistance-as-required in an IW. The effectiveness of the cBDI-based collaborative architecture is evaluated within a ROS-USARSim framework.

BDI Logics for BDI Architectures: Old Problems, New Perspectives

The mental attitudes of belief, desire, and intention play a central role in the design and implementation of autonomous agents. In 1987 Bratman proposed their integration into a belief-desire-intention (BDI) theory that was seminal in AI. Since then numerous approaches were built on the BDI paradigm, both practical (BDI architectures and BDI agents) and formal (BDI logics). The logical approaches that were most influential are due to Cohen&Levesque and to Rao&Georgeff. However, three fundamental problems remain up to now. First, the practical and the formal approaches evolved separately and neither fertilized the other. Second, only few formal approaches addressed some important issues such as the revision of intentions or the fundamentally paraconsistent nature of desires, and it seems fair to say that there is currently no consensual logical account of intentions. Finally, only few publications study the interaction between intention and other concepts that are naturally connected to intention, such as actions, planning, and the revision of beliefs and intentions. Our paper summarizes the state of the art, discusses the main open problems, and sketches how they can be addressed. We argue in particular that research on intention should be better connected to fields such as reasoning about actions, automated planning, and belief revision and update.

Using BDI Agents in Flexible Patterns for Cyber-Attacks over Electrical Power Infrastructures

This work aims to assess how the infrastructure of production & transport of the electric energy can be annihilated in a nation-state through a cyber attack, as a particular form of manifestation of cybernetic warfare. In this study we consider that the purpose of the cyber attack is only to remove servers from service, logically or physically. There are several standards that integrate the business side of a company with the level of supervision of industrial infrastructure and with the level of control engineering. These levels are integrated and are using different protocols. To simulate cyber-attacks between the countries with flexible potential escalation there are used BDI (Belief-Desire-Intention) agents which allow to separate treatment of different types of attacks. Inspired by the study of nuclear attacks implemented using the game theory, the flexibility of the BDI paradigm increases research potential of decision trees using this type of agent in other areas of human existence, like modeling human behavior at policy states makers or cyberspace executive users.

Integrating BDI Agents with Agent-Based Simulation Platforms

Agent-based models (ABMs) are increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire-Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the brains of an agent can be modelled in the BDI system in the usual way, while the body exists in the ABM system. The architecture is flexible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration of off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community.

DESIGNING A BDI AGENT REACTANT MODEL OF BEHAVIOURAL CHANGE INTERVENTION

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioural change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories. Also, using mathematical analysis the model was evaluated. The objective of the proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions. The model will be realized in an agent-based application that motivates children towards oral hygiene. The study explicitly depicts how agent’s behavioural factors interact to enhance behaviour change which will assist agent-based intervention designers to be able to design intervention that will be void of reactance.

Aborting, suspending, and resuming goals and plans in BDI agents

Intelligent agents designed to work in complex, dynamic environments such as e-commerce must respond robustly and flexibly to environmental and circumstantial changes, including the actions of other agents. An agent must have the capability to deliberate about appropriate courses of action, which may include reprioritising tasks--whether goals or associated plans--aborting or suspending tasks, or scheduling tasks in a particular order. In this article we study mechanisms to enable principled suspend, resuming, and aborting of goals and plans within a Belief-Desire-Intention (BDI) agent architecture. We give a formal and combined operational semantics for these actions in an abstract agent language (CAN), thus providing a general mechanism that can be incorporated into several BDI-based agent platforms. The abilities enabled by our semantics provides an agent designer greater flexibility to direct agent operation, offering a generic means to manage the status of goals. We demonstrate the reasoning abilities enabled on a document workflow scenario.

Building a Belief–Desire–Intention Agent for Modeling Neural Networks

This article presents an innovative learning technique for modeling nonlinear systems. Our belief–desire–intention algorithm for neural networks can effectively identify the parameters of most relevance to a model for the online adjustment of weights, neurons, and layers. We present a detailed explanation of each component in the proposed agent, and successfully apply our model to describe the lateral forces on a tire under a range of test conditions. The model output is compared to test data and the output of an existing neural network model. Our results demonstrate that the belief–desire–intention agent is reliable and applicable in nonlinear modeling and is superior to backpropagation neural networks.

Modeling and Simulation of Overtaking Maneuver Using BDI Agent

Overtaking other vehicles is the most complex maneuver and one of the most dangerous driving maneuver. A device that can be used to improve safety in the overtaking maneuver is the Overtaking Assistance System (OAS). The development of this device toward the Cooperative OAS requires modelling and simulation of overtaking maneuver based on multi-agent. In this paper the overtaking maneuver is modelled and simulated in three overtaking maneuvers techniques namely accelerative, flying, and 2+. Belief-desire-intention (BDI) agent architecture is used in modeling and simulation. The possibility of BDI agent to model the real situation is the main reason of the selection of the BDI agents in this study. The test results of model and simulation can represent the real situation and can be used in the development of cooperative OAS.

Translation of statechart agents into a BDI framework for MAS engineering

In the agent-oriented computing area, different programming paradigms have been to date proposed to design and implement multi-agent systems (MAS). Among them, the Belief-Desire-Intention (BDI) and Statecharts-based models have emerged as they effectively support the definition of rational and protocol-oriented agent behaviors, respectively. In this paper, we investigate the translation between such models by using two available agent modeling languages: PROFETA, which is based on the BDI agent model, and the agent-oriented Distilled StateCharts (DSC) formalism, which allows modeling agent behaviors based on Statecharts-like state machines. In particular, we propose a mapping from DSC to PROFETA that can enable visual programming of PROFETA programs to facilitate the development of PROFETA-based autonomous systems. A case-study is also developed to exemplify the proposed engineering approach.

On the Testability of BDI Agent Systems

Before deploying a software system we need to assure ourselves (and stakeholders) that the system will behave correctly. This assurance is usually done by testing the system. However, it is intuitively obvious that adaptive systems, including agent-based systems, can exhibit complex behaviour, and are thus harder to test. In this paper we examine this "obvious intuition" in the case of Belief-Desire-Intention (BDI) agents. We analyse the size of the behaviour space of BDI agents and show that although the intuition is correct, the factors that influence the size are not what we expected them to be. Specifically, we found that the introduction of failure handling had a much larger effect on the size of the behaviour space than we expected. We also discuss the implications of these findings on the testability of BDI agents.

A logic of intention and action for regular BDI agents based on bisimulation of agent programs

We address the problem of providing a computationally grounded semantics for belief, desire, intention (BDI) agents that explicitly relates intention to action, using as a basis for this connection a notion of bisimulation for agent programs. We first define regular BDI agents, a class of BDI agents inspired by the procedural reasoning system architecture, under the restriction that agent programs are representable as regular expressions. The operational semantics of regular agent programs is formalized using agent program execution graphs, an extension of the process graphs used to formalize regular processes. An agent's executed program represents an attempt to perform an intended plan and can include branches for both successful execution and the failure of action attempts; intended execution paths are defined in terms of successful executions, and intentions in terms of future successfully executed agent programs. We present Agent Dynamic Logic ($$\mathsf {ADL}$$ADL), a logic of intention and action that faithfully represents the operational semantics of regular BDI agents. $$\mathsf {ADL}$$ADL is a logic in the spirit of BDI logic but also includes the dynamic logic of actions and a reduction of the logic of intention to the logics of action and time. A main contribution of the paper is a completeness result for a subclass of finite $$\mathsf {ADL}$$ADL theories with explicit representations of agent plans.

Planning in BDI agents: a survey of the integration of planning algorithms and agent reasoning

AbstractAgent programming languages have often avoided the use of automated (first principles or hierarchical) planners in favour of predefined plan/recipe libraries for computational efficiency reasons. This allows for very efficient agent reasoning cycles, but limits the autonomy and flexibility of the resulting agents, oftentimes with deleterious effects on the agent's performance. Planning agents can, for instance, synthesise a new plan to achieve a goal for which no predefined recipe worked, or plan to make viable the precondition of a recipe belonging to a goal being pursued. Recent work on integrating automated planning with belief-desire-intention (BDI)-style agent architectures has yielded a number of systems and programming languages that exploit the efficiency of standard BDI reasoning, as well as the flexibility of generating new recipes at runtime. In this paper, we survey these efforts and point out directions for future work.

Introducing a novel model of belief–desire–intention agent for urban land use planning

Land use planning is a potentially demanding search and optimization task that has been challenged by numerous researchers in the field of spatial planning. Agent and multi-agent systems are examples of the modern concepts, which have been gaining more attention in challenging spatial issues recently. Although the efficiency of belief, desire, and intention (BDI) architecture of agents is validated in varieties of sciences, its uses in Geospatial Information Systems (GIS) and specifically among spatial planners is still burgeoning. In this paper, we attempted to integrate the concepts of BDI agent architecture into spatial issues; as a result, a novel spatial agent model is designed and implemented to analyze the urban land use planning. The proposed approach was checked in urban land use planning problems using a case study in a municipal area. The result of implementation showed the effects of spatial agents' behaviors such as intention, commitment, and interaction on their decision.

Supporting change impact analysis for intelligent agent systems

Software maintenance and evolution is an important and lengthy phase in the software life-cycle which can account for as much as two-thirds of the total software development costs. Intelligent agent technology has evolved rapidly over the past few years as evidenced by the increasing number of agent systems in many different domains. Intelligent agent systems with their distinct characteristics and behaviours introduce new problems in software maintenance. However, in contrast to a substantial amount of work in providing methodologies for analysing, designing and implementing agent-based systems, there has been very little work on maintenance and evolution of agent systems. A critical issue in software maintenance and evolution is change impact analysis: estimating the potential effects of changes before they are made as an agent system evolves. In this paper, we propose two distinct approaches to change impact analysis for the well-known and widely-developed Belief–Desire–Intention agent systems. On the one hand, our static technique computes the impact of a change by analysing the source code and identifying various dependencies within the agent system. On the other hand, our dynamic technique builds a representation of an agent’s behaviour by analysing its execution traces which consist of goals and plans, and uses this representation to estimate impacts. We have implemented both techniques and in this paper we also report on the experimental results that compare their effectiveness in practice.

Towards polite virtual agents using social reasoning techniques

ABSTRACTThe use of polite agents is a new approach in order to improve efficiency and naturalism in navigation for player characters in crowded virtual worlds. This paper aims to model the politeness of virtual humans using logic‐based approaches, subject to theory of politeness decomposed of conventional and interpersonal politeness. To do so, we propose a high‐level agent architecture combined with normative framework to model and reason about ‘polite’ behaviours in social situations. With this architecture, we demonstrate (i) specifying polite behaviours as a form of social norms; (ii) generating polite behaviours using social reasoning technique; (iii) deliberation with such norms in belief–desire–intention agents; and (iv) realising physical actions based on the decision. Implementation for social reasoning is achieved by InstAL, based on the semantics of answer set programming. Using experiments with simple collision avoidance model, we show the effectiveness of polite behaviour in navigation designed by such architecture, as well as the adequacy of this architecture for modelling theory of politeness in all circumstances. Copyright © 2013 John Wiley & Sons, Ltd.