A first-order coalition logic for BDI-agents

Coalition logic (CL) enables us to model the strategic abilities and specify what a group of agents can achieve whatever the other agents do. However, some rational mental attitudes of the agents are beyond the scope of CL such as the prestigious beliefs, desires and intentions (BDI) which is an interesting and useful epistemic notion and has spawned substantial amount of studies in multi-agent systems. In this paper, we introduce a first-order coalition BDI (FCBDI) logic for multi-agent systems, which provides a semantic glue that allows the formal embedding and interaction of BDI, coalition and temporal operators in a first-order language. We further introduce a semantic model based on the interpreted system model and present an axiomatic system that is proved sound and complete with respect to the semantics. Finally, it is shown that the computational complexity of its model checking in finite structures is PSPACE-complete.     

基于BDI模型的多机器人智能体系统设计

BDI模型是智能体设计的一种成熟结构，本文将BDI模型应用于多机器人智能体系统设计中．文章先从形式逻辑角度描述系统模型，然后讨论基于合同网的多机器人智能体的协作机制，最后给出基于BDI模型的多机器智能体的实现模型．     

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.     

社会Agent的BDO模型

目前MAS中思维状态的研究趋势是在个体模型中加入社会思维属性,研究社会承诺、依赖、联合意图的推理关系。在BDI模型中,以意图为中心的观点不描述社会Agent。该文分析了以竭力为中心的Agent思维状态研究存在的问题,提出MAS的分层模型,并提出以信念、愿望和义务作为基本思维属性（简称BDO）来描述Agent的思维状态和社会属性,给出了BDO逻辑和语义模型,考虑了奖励、惩罚、承诺和承诺解除问题,并对队、组织、组织意图等现象给出了描述。该文力图更自然地描述社会性Agent的思维状态和群体概念,是对Rao和Georgeff提出的BDI模型的改进。最后通过一个例子说明了BDO的表达能力。进一步的工作包括建立更为完善的语义模型、结合各个思维属性的动态修正语义给出BDO Agent的动态模型以及给出基于BDO逻辑框架的Agent／MAS实现结构。     

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.     

Group planning with time constraints

Embedding planning systems in real-world domains has led to the necessity of Distributed Continual Planning (DCP) systems where planning activities are distributed across multiple agents and plan generation may occur concurrently with plan execution. A key challenge in DCP systems is how to coordinate activities for a group of planning agents. This problem is compounded when these agents are situated in a real-world dynamic domain where the agents often encounter differing, incomplete, and possibly inconsistent views of their environment. To date, DCP systems have only focused on cases where agents’ behavior is designed to optimize a global plan. In contrast, this paper presents a temporal reasoning mechanism for self-interested planning agents. To do so, we model agents’ behavior based on the Belief-Desire-Intention (BDI) theoretical model of cooperation, while modeling dynamic joint plans with group time constraints through creating hierarchical abstraction plans integrated with temporal constraints network. The contribution of this paper is threefold: (i) the BDI model specifies a behavior for self interested agents working in a group, permitting an individual agent to schedule its activities in an autonomous fashion, while taking into consideration temporal constraints of its group members; (ii) abstract plans allow the group to plan a joint action without explicitly describing all possible states in advance, making it possible to reduce the number of states which need to be considered in a BDI-based approach; and (iii) a temporal constraints network enables each agent to reason by itself about the best time for scheduling activities, making it possible to reduce coordination messages among a group. The mechanism ensures temporal consistency of a cooperative plan, enables the interleaving of planning and execution at both individual and group levels. We report on how the mechanism was implemented within a commercial training and simulation application, and present empirical evidence of its effectiveness in real-life scenarios and in reducing communication to coordinate group members’ activities.     

Intentional learning agent architecture

Dealing with changing situations is a major issue in building agent systems. When the time is limited, knowledge is unreliable, and resources are scarce, the issue becomes more challenging. The BDI (Belief-Desire-Intention) agent architecture provides a model for building agents that addresses that issue. The model can be used to build intentional agents that are able to reason based on explicit mental attitudes, while behaving reactively in changing circumstances. However, despite the reactive and deliberative features, a classical BDI agent is not capable of learning. Plans as recipes that guide the activities of the agent are assumed to be static. In this paper, an architecture for an intentional learning agent is presented. The architecture is an extension of the BDI architecture in which the learning process is explicitly described as plans. Learning plans are meta-level plans which allow the agent to introspectively monitor its mental states and update other plans at run time. In order to acquire the intricate structure of a plan, a process pattern called manipulative abduction is encoded as a learning plan. This work advances the state of the art by combining the strengths of learning and BDI agent frameworks in a rich language for describing deliberation processes and reactive execution. It enables domain experts to specify learning processes and strategies explicitly, while allowing the agent to benefit from procedural domain knowledge expressed in plans.     

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.     

Proving the Asymmetry Thesis Principles for a BDI Agent-Oriented Programming Language

In this paper, we consider each of the nine principles of BDI logics as defined by Rao and Georgeff based on Bratman's asymmetry thesis, and we verify which ones are satisfied by Rao's AgentSpeak(L), a computable logic language inspired by the BDI architecture for cognitive agents. This is in line with Rao's original motivation for defining AgentSpeak(L): to bridge the gap between the theory and practice of BDI agent systems. In order to set the grounds for the proof, we first introduce a particular way in which to define the informational, motivational, and deliberative modalities of BDI logics for AgentSpeak(L) agents, according to its structural operational semantics (that we introduced in a recent paper). This provides a framework that can be used to investigate further properties of AgentSpeak(L) agents, contributing towards giving firm theoretical grounds for BDI agent programming.     

Autonomous agents with norms

In this paper we present some concepts and their relations that are necessary for modeling autonomous agents in an environment that is governed by some (social) norms. We divide the norms over three levels: the private level the contract level and the convention level. We show how deontic logic can be used to model the concepts and how the theory of speech acts can be used to model the generation of (some of) the norms. Finally we give some idea about an agent architecture incorporating the social norms based on a BDI framework.     

The adversarial activity model for bounded rational agents

Multiagent research provides an extensive literature on formal Beliefs-Desires-Intentions (BDI) based models describing the notion of teamwork and cooperation. However, multiagent environments are often not cooperative nor collaborative; in many cases, agents have conflicting interests, leading to adversarial interactions. This form of interaction has not yet been formally defined in terms of the agents mental states, beliefs, desires and intentions. This paper presents the Adversarial Activity model, a formal Beliefs-Desires-Intentions (BDI) based model for bounded rational agents operating in a zero-sum environment. In complex environments, attempts to use classical utility-based search methods with bounded rational agents can raise a variety of difficulties (e.g. implicitly modeling the opponent as an omniscient utility maximizer, rather than leveraging a more nuanced, explicit opponent model). We define the Adversarial Activity by describing the mental states of an agent situated in such environment. We then present behavioral axioms that are intended to serve as design principles for building such adversarial agents. We illustrate the advantages of using the model as an architectural guideline by building agents for two adversarial environments: the Connect Four game and the Risk strategic board game. In addition, we explore the application of our approach by analyzing log files of completed Connect Four games, and gain additional insights on the axioms’ appropriateness.     

Proving BDI Properties of Agent-Oriented Programming Languages: The asymmetry thesis principles in AgentSpeak(L)

In this paper, we consider each of the nine BDI principles defined by Rao and Georgeff based on Bratman's asymmetry thesis, and we verify which ones are satisfied by Rao's AgentSpeak(L), a logic programming language inspired by the BDI architecture for cognitive agents. In order to set the grounds for the proofs, we first introduce a rigorous way in which to define the informational, motivational, and deliberative modalities of BDI logics for AgentSpeak(L) agents, according to its structural operational semantics that we introduced in a recent paper. This computationally grounded semantics for the BDI modalities forms the basis of a framework that can be used to further investigate BDI properties of AgentSpeak(L) agents, and contributes towards establishing firm theoretical grounds for a BDI approach to agent-oriented programming.     

多Agent系统中实时消息传递机制

1 引言 Agent是具有信念、能力、决策和承诺等精神状态的实体,它的提出是以计算的社会学模型为背景,通过多个简单Agent协同工作,可以完成比较复杂的任务。分布式人工智能(DAI)的主要目标就是解决如何组织多Agent系统中的Agent,使它们能够协同工作以完成复杂问题求解。多Agent系统的关键问题是Agent间的协同,而根据计算社会学模型的观点,消息     

THE EFFECT OF NETWORK STRUCTURE ON DYNAMIC TEAM FORMATION IN MULTI-AGENT SYSTEMS

Previous studies of team formation in multi-agent systems have typically assumed that the agent social network underlying the agent organization is either not explicitly described or the social network is assumed to take on some regular structure such as a fully connected network or a hierarchy. However, recent studies have shown that real-world networks have a rich and purposeful structure, with common properties being observed in many different types of networks. As multi-agent systems continue to grow in size and complexity, the network structure of such systems will become increasing important for designing efficient, effective agent communities.
We present a simple agent-based computational model of team formation, and analyze the theoretical performance of team formation in two simple classes of networks (ring and star topologies). We then give empirical results for team formation in more complex networks under a variety of conditions. From these experiments, we conclude that a key factor in effective team formation is the underlying agent interaction topology that determines the direct interconnections among agents. Specifically, we identify the property of diversity support as a key factor in the effectiveness of network structures for team formation. Scale-free networks, which were developed as a way to model real-world networks, exhibit short average path lengths and hub-like structures. We show that these properties, in turn, result in higher diversity support; as a result, scale-free networks yield higher organizational efficiency than the other classes of networks we have studied.     

基于多智能体框架MAFS的目标识别技术

根据多智能体Agent的自治、协作和分布特性,并基于智能体的思维状态BDI模型,设计了Internet/Intranet支持的多智能体框架MAFS,其中包括MAFS中智能Agent的组成结构、Agent间的协作和通信、智能Agnet的知识表达和Agent计算等;同时提出了绘画式图象分割的策略和网络环境下图象识别的分布式处理思想,并在MAFS上建立了问题求解模型,还进行了桥梁目标识别。实验结果表明,基于多智能体杠架MAFS的目标识别具有高速、有效等优点,且对环境要求不高,并且易实现,从而实现了高速网络体系下的分布式目标处理。     

Simulation of intelligent hierarchical flexible manufacturing:batch job routing in operation overlapping

We present an approach to embedding expert systems within an object-oriented simulation environment that facilitates the creation of classes of expert system model elements that can be interfaced with other model components. Previous work has shown how distributed expert systems can be defined as modular components of simulations models. This article illustrates their usefulness in fractal architectures for flexible manufacturing, as proposed in the literature. Alternate structures (reconfigured structures) of a proposed hierarchical factory architecture can be generated through the recursive pruning process. Such rapid prototyping should greatly enhance the ability to investigate alternative architectural solutions to manufacturing problems in a timely manner     

An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. The presented agent architecture uses a one pass method to learn in a nonintrusive manner the user's particular behaviors and preferences for controlling the AIE. The agent learns the user's behavior by learning his particular rules and interval type-2 Membership Functions (MFs), these rules and MFs can then be adapted online incrementally in a lifelong learning mode to suit the changing environmental conditions and user preferences. We will show that the type-2 agents generated by our one pass learning technique outperforms those generated by genetic algorithms (GAs). We will present unique experiments carried out by different users over the course of the year in the Essex Intelligent Dormitory (iDorm), which is a real AIE test bed. We will show how the type-2 agents learnt and adapted to the occupant's behavior whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 agents while using smaller rule bases     

Deduction Systems for BDI Logics with Mental State Consistency

BDI Logics, introduced by Rao et al., have been used as the theoretical basis of specification and implementation of rational agents. The aim of our research is to make full use of the expressive power of BDI Logics as executable specification languages of rational agents. To this end, we previously presented deduction systems for CTL-based propositional BDI Logics using sequent calculus. Since these systems have a decision algorithm that is extended from Wang's algorithm, they are suitable for applications such as automatic proving. However, they do not incorporate mental state consistency features, which are important for dealing with rational agents. In this paper, we extend our deduction systems by introducing mental state consistency features and explain their soundness and completeness. This approach allows us to check and prove the specifications and properties described by BDI Logics for rational agents.     

Automated Test Case Generation for BDI Agents

We propose a coverage oriented test case generation methodology for BDI multi-agent systems. The coverage criteria involve plans and nodes within plans of multi-agent systems. We organise the criteria into a subsumption hierarchy to show the coverage relationships between the criteria. Then we apply the criteria on multi-agent systems to analyse some empirical data. The data analysed is the effect on the number of test cases generated automatically for each criterion. We use a tool, BDITESTER, to obtain the empirical data and to show that our proposal is pragmatic. Finally, we suggest some guidelines to select a criterion to automatically generate test cases for BDI agents.     

一种适合开放环境的Agent体系结构

通过对著名的BDIAgent模型进行改进,并引入元级推理机制而给出了一个适合开放环 境的Agent体系结构。该体系结构具有高效、可扩展的、易于实现等特点。同时,该体系结构还解决 了Agent构造中的一系列难题。