Showing papers in "Autonomous Agents and Multi-Agent Systems in 2008"
TL;DR: The model is that agents use their social network to reach information and their trust relationships to filter it and it is identified that network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system.
Abstract: In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.
413 citations
TL;DR: A BDI-based agent-oriented programming language that facilitates the implementation of multi-agent systems consisting of individual agents that may share and access external environments, called 2APL (A Practical Agent Programming Language).
Abstract: This article presents a BDI-based agent-oriented programming language, called 2APL (A Practical Agent Programming Language). This programming language facilitates the implementation of multi-agent systems consisting of individual agents that may share and access external environments. It realizes an effective integration of declarative and imperative style programming by introducing and integrating declarative beliefs and goals with events and plans. It also provides practical programming constructs to allow the generation, repair, and (different modes of) execution of plans based on beliefs, goals, and events. The formal syntax and semantics of the programming language are given and its relation with existing BDI-based agent-oriented programming languages is discussed.
394 citations
TL;DR: The notions of artifact in the A&A meta-model are defined, how it affects the notion of intelligence in MAS is discussed, and its application to a number of agent-related research fields are shown.
Abstract: In this article we focus on the notion of artifact for agents in multi-agent systems (MAS) as a basis for a new meta-model promoting the modelling and engineering of agent societies and MAS environment as first-class entities. Its conceptual foundations lay upon theories and results coming from computational sciences as well as from organisational and cognitive sciences, psychology, computer supported cooperative work (CSCW), anthropology and ethology. In the resulting agents & artifacts (A&A) meta-model, agents are the (pro-)active entities in charge of the goals/tasks that altogether build up the whole MAS behaviour, whereas artifacts are the reactive entities providing the services and functions that make individual agents work together in a MAS, and that shape agent environment according to the MAS needs. After presenting the scientific background, we define the notions of artifact in the A&A meta-model, discuss how it affects the notion of intelligence in MAS, and show its application to a number of agent-related research fields.
366 citations
TL;DR: The analysis indicates that despite strong industrial involvement in this field, the full potential of the agent technology has not been fully utilized yet and that not all of the developed agent concepts and agent techniques have been completely exploited in industrial practice.
Abstract: This paper reports on industrial deployment of multi-agent systems and agent technology. It provides an overview of several application domains and an in-depth presentation of four specific case studies. The presented applications and deployment domains have been analyzed. The analysis indicates that despite strong industrial involvement in this field, the full potential of the agent technology has not been fully utilized yet and that not all of the developed agent concepts and agent techniques have been completely exploited in industrial practice. In the paper, the key obstacles for wider deployments are listed and potential future challenges are discussed.
239 citations
TL;DR: Five different formal frameworks, three different optimal algorithms, as well as a series of approximation techniques are analyzed to provide interesting insights into the structure of decentralized problems, the expressiveness of the various models, and the relative advantages and limitations of the different solution techniques.
Abstract: Over the last 5 years, the AI community has shown considerable interest in decentralized control of multiple decision makers or "agents" under uncertainty. This problem arises in many application domains, such as multi-robot coordination, manufacturing, information gathering, and load balancing. Such problems must be treated as decentralized decision problems because each agent may have different partial information about the other agents and about the state of the world. It has been shown that these problems are significantly harder than their centralized counterparts, requiring new formal models and algorithms to be developed. Rapid progress in recent years has produced a number of different frameworks, complexity results, and planning algorithms. The objectives of this paper are to provide a comprehensive overview of these results, to compare and contrast the existing frameworks, and to provide a deeper understanding of their relationships with one another, their strengths, and their weaknesses. While we focus on cooperative systems, we do point out important connections with game-theoretic approaches. We analyze five different formal frameworks, three different optimal algorithms, as well as a series of approximation techniques. The paper provides interesting insights into the structure of decentralized problems, the expressiveness of the various models, and the relative advantages and limitations of the different solution techniques. A better understanding of these issues will facilitate further progress in the field and help resolve several open problems that we identify.
225 citations
TL;DR: A key component of that framework is presented, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems, which defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent's capabilities.
Abstract: Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member's capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system's organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent's capabilities.
142 citations
TL;DR: It is argued that the introduction of obligations can provide a new reading of the concepts of intention and intentionality and it is shown that the notion of social agent either requires more complex computations or has some philosophical drawbacks.
Abstract: In this paper we follow the BOID (Belief, Obligation, Intention, Desire) architecture to describe agents and agent types in Defeasible Logic. We argue, in particular, that the introduction of obligations can provide a new reading of the concepts of intention and intentionality. Then we examine the notion of social agent (i.e., an agent where obligations prevail over intentions) and discuss some computational and philosophical issues related to it. We show that the notion of social agent either requires more complex computations or has some philosophical drawbacks.
141 citations
TL;DR: This special issue contains four selected and revised papers from the second international workshop on normative multiagent systems, for short NorMAS07 and discusses the shift in the research community from a legal to an interactionist view on normativemultiagent systems.
Abstract: This special issue contains four selected and revised papers from the second international workshop on normative multiagent systems, for short NorMAS07 (Boella et al. (eds) Normative multiagent systems. Dagstuhl seminar proceedings 07122, 2007), held at Schloss Dagstuhl, Germany, in March 2007. At the workshop a shift was identified in the research community from a legal to an interactionist view on normative multiagent systems. In this editorial we discuss the shift, examples, and 10 new challenges in this more dynamic setting, which we use to introduce the papers of this special issue.
134 citations
TL;DR: This paper presents a new reward evaluation method that provides a visualization of the tradeoff between the level of coordination among the agents and the difficulty of the learning problem each agent faces, and shows that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting good rewards.
Abstract: The ability to analyze the effectiveness of agent reward structures is critical to the successful design of multiagent learning algorithms. Though final system performance is the best indicator of the suitability of a given reward structure, it is often preferable to analyze the reward properties that lead to good system behavior (i.e., properties promoting coordination among the agents and providing agents with strong signal to noise ratios). This step is particularly helpful in continuous, dynamic, stochastic domains ill-suited to simple table backup schemes commonly used in TD(?)/Q-learning where the effectiveness of the reward structure is difficult to distinguish from the effectiveness of the chosen learning algorithm. In this paper, we present a new reward evaluation method that provides a visualization of the tradeoff between the level of coordination among the agents and the difficulty of the learning problem each agent faces. This method is independent of the learning algorithm and is only a function of the problem domain and the agents' reward structure. We use this reward property visualization method to determine an effective reward without performing extensive simulations. We then test this method in both a static and a dynamic multi-rover learning domain where the agents have continuous state spaces and take noisy actions (e.g., the agents' movement decisions are not always carried out properly). Our results show that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting good rewards, compared to running a full simulation. In addition, this method facilitates the design and analysis of new rewards tailored to the observational limitations of the domain, providing rewards that combine the best properties of traditional rewards.
128 citations
TL;DR: KB-ORG is presented: a fully automated, knowledge-based organization designer for multi-agent systems that uses situational parameters as well as application-level and coordination-level knowledge to generate organization designs and designs that are effective when given different organizational requirements and environmental expectations.
Abstract: The ability to create effective multi-agent organizations is key to the development of larger, more diverse multi-agent systems. In this article we present KB-ORG: a fully automated, knowledge-based organization designer for multi-agent systems. Organization design is the process that accepts organizational goals, environmental expectations, performance requirements, role characterizations, and agent descriptions and assigns roles to each agent. These long-term roles serve as organizational-control guidelines that are used by each agent in making moment-to-moment operational control decisions. An important aspect of KB-ORG is its efficient, knowledge-informed search process for designing multi-agent organizations. KB-ORG uses both application-level and coordination-level organization design knowledge to explore the combinatorial search space of candidate organizations selectively. KB-ORG also delays making coordination-level organizational decisions until it has explored and elaborated candidate application-level agent roles. This approach significantly reduces the exploration effort required to produce effective designs as compared to modeling and evaluation-based approaches that do not incorporate design expertise. KB-ORG designs are not restricted to a single organization form such as a hierarchy, and the organization designs described here contain both hierarchical and peer-to-peer elements. We use examples from the distributed sensor network (DSN) domain to show how KB-ORG uses situational parameters as well as application-level and coordination-level knowledge to generate organization designs. We also show that KB-ORG designs effective, yet substantially different, organizations when given different organizational requirements and environmental expectations.
69 citations
TL;DR: New diversification techniques for the second approach in order to get better results are proposed and a new promising approach combining the two latter ones are proposed.
Abstract: The Flexible Job Shop problem is among the hardest scheduling problems It is a generalization of the classical Job Shop problem in that each operation can be processed by a set of resources and has a processing time depending on the resource used The objective is to assign and to sequence the operations on the resources so that they are processed in the smallest time In our previous work, we have proposed two Multi-Agent approaches based on the Tabu Search (TS) meta-heuristic Depending on the location of the optimisation core in the system, we have distinguished between the global optimisation approach where the TS has a global view on the system and the local optimisation approach (FJS MATSLO) where the optimisation is distributed among a collection of agents, each of them has its own local view In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones Experimental results are also presented in this paper in order to evaluate these new techniques
TL;DR: In this article, an ontology of social collectives that includes social agents, plans, norms, and the conceptual relations between them is presented. But the ontology is not defined in terms of plans and norms.
Abstract: Based on the paradigm of Constructive Descriptions and Situations, we introduce NIC, an ontology of social collectives that includes social agents, plans, norms, and the conceptual relations between them Norms are distinguished from plans, and their relations are formalized A typology of social collectives is also proposed, including collection of agents, knowledge community, intentional collective, and normative intentional collective NIC, represented as a first-order theory as well as a description logic for applications requiring automated reasoning, provides the expressivity to talk about the contexts (social, informational, circumstantial, and conceptual), in which collectives make and produce sense within the interplay of plans and norms
TL;DR: A normative language to specify norms is presented and the implementation of such norms is proposed by using a rule-based system to govern the behavior of the agents according to the norm.
Abstract: Open multi-agent systems composed of heterogeneous, autonomous and independently designed agents are usually governed by a set of norms. The established norms regulate the behavior of the agents by pointing out their permissions, prohibitions and obligations. This paper presents a normative language to specify norms and proposes the implementation of such norms by using a rule-based system. The implementation is achieved by automatically transforming the specification of each norm of the system into a set of rules used to govern the behavior of the agents according to the norm. The governance system is able to activate and deactivate norms, to point out the norms violations and fulfillments and to inform about punishments and rewards.
TL;DR: A formal argumentation-based model is proposed that constructs arguments in favor of each possible classification of an example, evaluates them, and determines among the conflicting arguments the acceptable ones, and a “valid” classification of the example is suggested.
Abstract: Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent/incomplete/uncertain knowledge, based on the construction and the comparison of arguments. In this paper, we apply this approach to the classification problem, whose purpose is to construct from a set of training examples a model that assigns a class to any new example. We propose a formal argumentation-based model that constructs arguments in favor of each possible classification of an example, evaluates them, and determines among the conflicting arguments the acceptable ones. Finally, a "valid" classification of the example is suggested. Thus, not only the class of the example is given, but also the reasons behind that classification are provided to the user as well in a form that is easy to grasp. We show that such an argumentation-based approach for classification offers other advantages, like for instance classifying examples even when the set of training examples is inconsistent, and considering more general preference relations between hypotheses. In the particular case of concept learning, the results of version space theory developed by Mitchell are retrieved in an elegant way in our argumentation framework. Finally, we show that the model satisfies the rationality postulates identified in argumentation literature. This ensures that the model delivers sound results.
TL;DR: In this article, the authors propose a formal resolution mechanism for prioritized conditional imperatives in deontic logic, where the agent can be expected to follow a maximal subset of the norms if these norms come into conflict.
Abstract: The sentences of deontic logic may be understood as describing what an agent ought to do when faced with a given set of norms. If these norms come into conflict, the best the agent can be expected to do is to follow a maximal subset of the norms. Intuitively, a priority ordering of the norms can be helpful in determining the relevant sets and resolve conflicts, but a formal resolution mechanism has been difficult to provide. In particular, reasoning about prioritized conditional imperatives is overshadowed by problems such as the `order puzzle' that are not satisfactorily resolved by existing approaches. The paper provides a new proposal as to how these problems may be overcome.
TL;DR: Two approaches for adaptive task assignment that are characteristic for two classical families of task assignment approaches are studied, DynCNET and FiTA, which are both protocol-based approaches that extends Standard Contract Net.
Abstract: Task assignment in multi-agent systems is a complex coordination problem, in particular in systems that are subject to dynamic and changing operating conditions. To enable agents to deal with dynamism and change, adaptive task assignment approaches are needed. In this paper, we study two approaches for adaptive task assignment that are characteristic for two classical families of task assignment approaches. FiTA is a field-based approach in which tasks emit fields in the environment that guide idle agents to tasks. DynCNET is a protocol-based approach that extends Standard Contract Net (CNET). In DynCNET, agents use explicit negotiation to assign tasks. We compare both approaches in a simulation of an industrial automated transportation system. Our experiences show that: (1) the performance of DynCNET and FiTA are similar, while both outperform CNET; (2) the complexity to engineer DynCNET is similar to FiTA but much more complex than CNET; (3) whereas task assignment with FiTA is an emergent solution, DynCNET specifies the interaction among agents explicitly allowing engineers to reason on the assignment of tasks, (4) FiTA is inherently robust to message loss while DynCNET requires substantial additional support. The tradeoff between (3) and (4) is an important criteria for the selection of an adaptive task assignment approach in practice.
TL;DR: This article proposes to use constraint processing to formalize preventive anticipation in the context of multi-agent coordination, particularly preventive anticipation which consists of anticipating undesirable future situations in order to avoid them.
Abstract: Anticipation is a general concept used and applied in various domains. Many studies in the field of artificial intelligence have investigated the capacity for anticipation. In this article, we focus on the use of anticipation in multi-agent coordination, particularly preventive anticipation which consists of anticipating undesirable future situations in order to avoid them. We propose to use constraint processing to formalize preventive anticipation in the context of multi-agent coordination. The resulting algorithm allows any action that may induce an undesirable future state to be detected upstream of any multi-agent coordination process. Our proposed method is instantiated in a road traffic simulation tool. For the specific question of simulating traffic at road junctions, our results show that taking anticipation into account allows globally realistic behaviors to be reproduced without provoking gridlock between the simulated vehicles.
TL;DR: A new representation for capturing multi-agent designs is described, and in particular it is shown how quantitative information can form the basis of a flexible, predictive organizational model.
Abstract: As the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants' interactions. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents different roles, responsibilities and peers. These additional constraints can allow agents to operate more efficiently within the system by limiting the options they must consider. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand the behavior of competing candidate designs. In this article, we describe a new representation for capturing such designs, and in particular we show how quantitative information can form the basis of a flexible, predictive organizational model. The representation is capable of capturing a wide range of multi-agent characteristics in a single, succinct model. We demonstrate the language's capabilities and efficacy by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. These same models also describe the space of possible organizations in those domains and several search techniques are described that can be used to explore this space, using those quantitative predictions and context-specific definitions of utility to evaluate alternatives. The results of such a search process can be used to select the organizational design most appropriate for a given situation.
TL;DR: This paper analyzes the key issue of defining suitable “component” models for autonomic communication services, and discusses the strict relation between such models and agent models.
Abstract: The continuous growth in ubiquitous and mobile network connectivity, together with the increasing number of networked devices populating our everyday environments, call for a deep rethinking of traditional communication and service architectures. The emerging area of autonomic communication addresses such challenging issues by trying to identify novel flexible network architectures, and by conceiving novel conceptual and practical tools for the design, development, and execution of "autonomic" (i.e., self-organizing, self-adaptive and context-aware) communication services. In this paper, after having introduced the general concepts behind autonomic communication and autonomic communication services, we analyze the key issue of defining suitable "component" models for autonomic communication services, and discuss the strict relation between such models and agent models. On this basis, we survey and compare different approaches, and eventually try to synthesize the key desirable characteristics that one should expect from a general-purpose component model for autonomic communication services. The key message we will try to deliver is that current research in software agents and multi-agent systems have the potential for playing a major role in inspiring and driving the identification of such a model, and more in general for influencing and advancing the whole area of autonomic communication.
TL;DR: A distributed abductive reasoning system is described, which is called DARE, and its implementation in the multi-threaded Qu-Prolog variant of Prolog is described to prove the soundness of the algorithm it uses and its completeness in relation to non-distributed abductionive reasoning.
Abstract: Abductive reasoning is a well established field of Artificial Intelligence widely applied to different problem domains not least cognitive robotics and planning. It has been used to abduce high-level descriptions of the world from robot sense data, using rules that tell us what sense data would be generated by certain objects and events of the robots world, subject to certain constraints on their co-occurrence. It has also been used to abduce actions that might result in a desired goal state of the world, using descriptions of the normal effects of these actions, subject to constraints on the action combinations. We can generalise these applications to a multi-agent context. Several robots can collaboratively try to abduce an agreed higher-level description of the state of the world from their separate sense data consistent with their collective constraints on the abduced description. Similarly, multi-agent planning can be accomplished by the abduction of the actions of a collective plan where each agent uses its own description of the effect of its actions within the plan, such that the constraints on the actions of all the participating agents are satisfied. To address this class of problems, we need to generalise the single agent abductive reasoning algorithm to a distributed abductive inference algortihm. In addition, if we want to investigate applications in which the set of collaborating robots/agents is open, we need an algorithm that allows agents to join or leave the collaborating group whilst a particular inference is under way, but which still produces sound abductive inferences. This paper describes such a distributed abductive reasoning system, which we call DARE, and its implementation in the multi-threaded Qu-Prolog variant of Prolog. We prove the soundness of the algorithm it uses and we discuss its completeness in relation to non-distributed abductive reasoning. We illustrate the use of the algorithm with a multi-agent meeting scheduling example. The task is open in that the actual agents who need to attend is not determined in advance. Each individual agent has its own constraints on the possible meeting time and concerning which other agents must or must attend the meeting, if it attends. The algorithm selects the agents to attend and ensures that the constraints of each of the attending agents are satisfied.
TL;DR: The role of standards in the adoption of new technologies, the description of the agent standards landscape, and the development and diffusion of agent technologies with that of object-oriented programming are examined.
Abstract: Despite several examples of deployed agent systems, there remain barriers to the large-scale adoption of agent technologies. In order to understand these barriers, this paper considers aspects of marketing theory which deal with diffusion of innovations and their relevance to the agents domain and the current state of diffusion of agent technologies. In particular, the paper examines the role of standards in the adoption of new technologies, describes the agent standards landscape, and compares the development and diffusion of agent technologies with that of object-oriented programming. The paper also reports on a simulation model developed in order to consider different trajectories for the adoption of agent technologies, with trajectories based on various assumptions regarding industry structure and the existence of competing technology standards. We present details of the simulation model and its assumptions, along with the results of the simulation exercises.
TL;DR: A novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings and proves the computational adequacy of the method by studying the complexity of the problem of finding Pareto-efficient solutions in the authors' setting.
Abstract: We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce $${\mathcal{P}}({\mathcal{N}})$$ , a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from well-known approaches that describe issues as uncorrelated. Through $${\mathcal{P}}({\mathcal{N}})$$ it is possible to represent buyer's request, seller's supply and their respective preferences as formulas endowed with a formal semantics, e.g., "if I spend more than 30000 � for a sedan then I want more than a two-years warranty and a GPS system included". We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting.
TL;DR: It is shown that this multi-agent system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommender.
Abstract: Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommender.
TL;DR: The Model of Organisational Change using Agents (MOChA) is presented as a means to formally specify, check and simulate organisations using agents, particularly with a view to determining the impact of influence on the operation of an organisation.
Abstract: Influence is a phenomenon underpinning many types of interactions in both human and artificial organisations, and has a significant impact on the operation of the organisation. If influence can be examined at the organisational level, instead of at the level of the agents involved, engineers can better understand an organisation's robustness to structural, behavioural and population changes. In this paper we present the Model of Organisational Change using Agents (MOChA) as a means to formally specify, check and simulate organisations using agents, particularly with a view to determining the impact of influence on the operation of an organisation. This formalisation of influence is not specific to our model, and is relevant and adaptable to any organisational model in which explicit relationships among roles of agents are formed.
TL;DR: It is believed that the convergence of two promising technologies (virtualization and mobile agents) can create cost-effective, robust, reliable, and easy-to-manage frameworks.
Abstract: Creating a secure framework for mobile agents that leverage such an efficient tool's usage is yet to be found [Farmer WM, Guttman JD, Swarup V (1996) Proceedings of the 19th national information systems security conference, Tardo J, Valente L (1996) Mobile agent security and telescript, IEEE CompCon]. There are some available approaches to prevent agent-to-agent and agent-to-host type attacks; however, host-to-agent type attacks prevention is still at large [Jansen W, Karygiannis T NIST special publication 800-19--Mobile agent security, National Institute of Standards and Technology]. In this paper, we have implemented a framework in which both agents and hosts are protected. The three virtualization techniques (vserver, vmware, and xen) are utilized as host environments to create a secure, scalable, and efficient framework. Three agent platforms (ajanta, aglets, and sage) are installed on these virtual environments and tested for attacks. Along with a trusted server, our framework claims to be a solution to prevent host-to-agent type attacks during execution as well as most of the other types of attacks. As a result, we believe that the convergence of two promising technologies (virtualization and mobile agents) can create cost-effective, robust, reliable, and easy-to-manage frameworks.
TL;DR: This article introduces nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution) and proposes a new temporal logic called α-ctl, which is implemented as a side effect of model checking.
Abstract: Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic ctl turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the ctl's semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called ?-ctl. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.
TL;DR: Although no task variety leads to specialisation and high task variety leading to generalisation, in general, performance is better when task variety is low and in case of moderate variety the opposite is true.
Abstract: Multi-agent simulation is applied to explore how different types of task variety cause workgroups to change their task allocation accordingly. We studied two groups, generalists and specialists. We hypothesised that the performance of the specialists would decrease when task variety increases. The generalists, on the other hand, would perform better in a high task variety condition. The results show that these hypotheses were only partly supported because both learning and motivational effects changed the task allocation process in a much more complex way. We conclude that although no task variety leads to specialisation and high task variety leads to generalisation, in general, performance is better when task variety is low. Further, in case of no task variety, specialists outperform generalists. In case of moderate variety the opposite is true. With high task variety, since there is no space for any expertise and motivational development, the behaviour of specialists and generalists becomes more similar, and, consequently also their performance.
Journal Article•
TL;DR: An agent-based approach to design a Transportation Regulation Support System (TRSS) based on a multi-agent modeling of a urban transportation network to integrate the functionalities of the existing information system with thefunctionalities of a decision support system is presented.
Abstract: This paper presents an agent-based approach to design a Transportation Regulation Support System (TRSS). Based on a multi-agent modeling of a urban transportation network, the objective of our approach is to integrate the functionalities of the existing information system with the functionalities of a decision support system. The TRSS monitors the network activity and adjusts itself to the environment changes, that is to say it automatically detects incoherent data (regulation under normal conditions) and tra c disturbances and then it automatically proposes solutions to optimize the tra c ow (regulation under disturbed conditions). To demonstrate our approach, a transportation regulation support system called SATIR (Systeme Automatique de Traitement des Incidents en Reseau- Automatic System for Network Incident Processing) is presented. SATIR has been tested on the Brussel transportation network (STIB). Lastly, we show how using the multi-agent paradigm opens perspectives regarding the development of new functionalities to improve the management of a bus network.
TL;DR: A new theory of intention representation which is based on a structure called a Dynamic Intention Structure (DIS), which provides a first order semantics for the resulting logic and explores the problem of logical consequence and intention revision.
Abstract: This article introduces a new theory of intention representation which is based on a structure called a Dynamic Intention Structure (DIS). The theory of DISs was motivated by the problem of how to properly represent incompletely specified intentions and their evolution. Since the plans and intentions of collaborating agents are most often elaborated incrementally and jointly, elaboration processes naturally involve agreements among agents on the identity of appropriate agents, objects and properties that figure into their joint plans. The paper builds on ideas from dynamic logic to present a solution to the representation and evolution of agent intentions involving reference to incompletely specified and, possibly, mutually dependent intentions, as well as the objects referenced within those intentions. It provides a first order semantics for the resulting logic. A companion paper extends further the logical form of DISs and explores the problem of logical consequence and intention revision.
TL;DR: This special issue collects four articles dealing with some of the main issues that arose during the three Technical Forum Group meetings held in 2004 and 2005, which were organised and sponsored by the European FP6 Coordination Action AgentLink III.
Abstract: This paper introduces the Special Issue of the Journal of Autonomous Agents and Multi-Agent Systems on Foundations, Advanced Topics and Industrial Perspectives of Multi-Agent Systems. This special issue collects four articles dealing with some of the main issues that arose during the three Technical Forum Group meetings held in 2004 and 2005, which were organised and sponsored by the European FP6 Coordination Action AgentLink III.