Smart places: Multi-agent based smart mobile virtual community management system

Now-a-days advances in mobile device technology aim to build complex computational systems providing a maximum level of flexibility, decentralization, simplest form of interactivity, and ease of use. Recently, the launch of the agent-oriented platform JaCaMo and its Android client based platform JaCa-Android have provided an appropriate level of abstraction to build smart mobile client server systems providing these attributes. By using these platforms, we have developed a multi-agent based Smart Mobile Virtual Community Management System (SMVCMS) that makes it possible to provide a decentralized and open management of virtual communities. This paper addresses the design and architecture of our multi-agent server and client application. It elaborates different features of our system; such as how a participant in virtual communities is supported by a Jason agent that encapsulates the logic and the control of the participation in a virtual community (such as publishing posts, notifying members, making recommendations for the user, etc.). It also discusses how the set of CArtAgOartifacts provides the basic functionalities and operations giving access to the functionalities for knowledge exchange in virtual communities, and personal agents onAndroid exploit these artifacts to execute their tasks while achieving their individual and collective goals. We have employed SMVCMS in the context of Smart Cities and found that the system fulfills the desired goals, such as decentralization of community management, personalized automatic management and discovery of communities, autonomy of agents and flexibility so that any agent can create its own community with the maximum level of ease.     

Multi-agent Mediator architecture for distributed manufacturing

A generic Mediator architecture for distributed task planning and coordination has been developed using multi-agent paradigms. In this approach, agents function autonomously as independent computing processes, and dynamic virtual clusters coordinate the agent's activities and decision making. This coordination involves dynamically created coordination agents and resource agents concurrently. The Mediator architecture contains three levels of these coordination agents: the template mediator, the data-agent manager, and the active mediator. The template mediator is the top-level global coordinator. This agent contains both the templates and the cloning mechanism to create the successively lower-level agents. Task plans are decomposed successively into subtasks, which are allocated to dynamically created agent clusters coordinated through data-agent managers and active mediators. Coordination of agent activity takes place both among the clusters and within each cluster. The system dynamically adapts to evolving manufacturing tasks, with virtual agent clusters being created as needed, and destroyed when their tasks are completed. The mediator architecture and related mechanisms are demonstrated using an intelligent manufacturing scheduling application. Both the machines and the parts involved in this production system are considered as intelligent agents. These agents use a common language protocol based on the Knowledge Query Manipulation Language (KQML). The generic Mediator approach can be used for other distributed organizational systems beyond the intelligent manufacturing application it was originally developed for.     

Action dependencies in privacy-preserving multi-agent planning

Collaborative privacy-preserving planning (CPPP) is a multi-agent planning task in which agents need to achieve a common set of goals without revealing certain private information. In many CPPP algorithms, the individual agents reason about a projection of the multi-agent problem onto a single-agent classical planning problem. For example, an agent can plan as if it controls the public actions of other agents, ignoring any private preconditions and effects theses actions may have, and use the cost of this plan as a heuristic estimate of the cost of the full, multi-agent plan. Using such a projection, however, ignores some dependencies between agents’ public actions. In particular, it does not contain dependencies between public actions of other agents caused by their private facts. We propose a projection in which these private dependencies are maintained. The benefit of our dependency-preserving projection is demonstrated by using it to produce high-level plans in a new privacy-preserving planner, and as a heuristic for guiding forward search privacy-preserving algorithms. Both are able to solve more benchmark problems than any other state-of-the-art privacy-preserving planner. This more informed projection does not explicitly expose any private fact, action, or precondition. In addition, we show that even if an adversary agent knows that an agent has some private objects of a given type (e.g., trucks), it cannot infer the number of such private objects that the agent controls. This introduces a novel form of strong privacy, which we call object-cardinality privacy, that is motivated by real-world requirements.     

Control of cooperative manipulator-endowed systems under high-level tasks and uncertain dynamics

This paper considers the problem of distributed motion- and task-planning of multi-agent and multi-agent-object systems under temporal-logic-based tasks and uncertain dynamics. We focus on manipulator-endowed robotic agents that can interact with their surroundings. We present first continuous control algorithms for multi-agent navigation and cooperative object manipulation that exhibit the following properties. First, they are distributed in the sense that each agent calculates its own control signal from local interaction with the other agents and the environment. Second, they guarantee safety properties in terms of inter-agent collision avoidance and obstacle avoidance. Third, they adapt on-the-fly to dynamic uncertainties and are robust to exogenous disturbances. The aforementioned algorithms allow the abstraction of the underlying system to a finite-state representation. Inspired by formal-verification techniques, we use such a representation to derive plans for the agents that satisfy the given temporal-logic tasks. Various simulation results and hardware experiments verify the efficiency of the proposed algorithms.     

Developing Real Applications With Agent Technologies

In this paper, we present an agent library to develop multi-agent applications. Such an agent library has been realised taking into account FIPA specifications. FIPA specifications give the minimum amount of technology deemed necessary for the management of agents in an open agent system, including agent roles, an agent communication language, an agent management content language, and a standard way to interact with non-agentised software.Therefore, the library offers the prototypes of the agents necessary for the management of a FIPA agent open system and an agent prototype that the user can specialise to build the other agents necessary for her/his application. The library has been implemented both in C++ and Java. The two implementations support a logical distribution, i.e., the agents correspond to different threads, and a physical distribution, i. e., agents of the same applications run on different machines. The Java library has been used to develop a multi-agent system, that facilitates users to use a video on demand service, and a first prototype of the system is under experimentation. The C++ library is used for the development of a multi-agent system that should integrate the different software modules performing sensory data interpretation, planning, faults diagnosis, . . . of a robot working in a space station.     

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.     

Trust-Based Facilitator: Handling Word-of-Mouth Trust for Agent-Based E-Commerce

This paper proposes a facilitator which finds capable and trustworthy partners on behalf of client users (agents), which helps agents form and maintain e-partnerships for electronic commerce. Unlike existing capability-based facilitators or matchmakers, the facilitator collects and maintains private word-of-mouth trust information as well as capabilities from each agent and uses the information for personalized trust-based facilitation for each agent, which is performed through the facilitation protocols and trust propagation mechanism. Compared to other existing trust mechanisms, the characteristics of trust which this facilitator handles are personalized-collaborative-subjective-qualitative-private. The facilitator is implemented as a JATLite multi-agent system and a FIPA-OS based multi-agent system, and is evaluated in terms of the complexity and characteristics. The example of usage is shown in the area of construction supply-chain coordination.     

Participant Selection for Short-term Collaboration in Open Multi-agent systems

In multi-agent systems, agents coordinate their behaviour and work together to achieve a shared goal through collaboration. However, in open multi-agent systems, selecting qualified participants to form effective collaboration communities is challenging. In such systems, agents do not have access to complete domain knowledge, they leave and join the systems unpredictably. More importantly, agents are mostly self-interested and have multiple goals and policies that may be even conflicting with others, which makes the participant selection even more challenging.Many of the current approaches are not applicable in constantly evolving open systems, where their performance will be affected by any unpredictable behaviour, agents’ lack of complete domain knowledge and the impossibility of having a central coordinator agent. In open systems, agents require a mechanism that enables them to dynamically change their perception of the environment and observe their neighbouring agents, so that they can identify qualified collaboration participants that have no conflicting goals and to balance their level of cooperation and self-interest.In this paper, we propose OPSCO, as a solution for On-demand Participant Selection for Short-term Collaboration in Open multi-agent systems. Unlike the existing research, we do not assume any predefined setting for agents’ structure in the system and do not have access to complete domain knowledge and allow each agent to build a dynamic dependency model and maintain when there is a change in the system. The model captures the agent’s most recent dependency structure of goals and policies with its neighbouring agents. It enables them to identify and select a qualified non-conflicting set of participants.OPSCO is evaluated in a real world open system smart grid and constrained resource sharing case studies. OPSCO outperforms other methods by selecting a qualified non-conflicting set of agents to collaborate. OPSCO balances the self-interest and level of cooperation and decreases failure in the overall agents’ goals (individual/shared).     

Evolutionary patterns of agent organizations

Problems approached by multi-agent systems are typically complex. It is usually difficult to know at system design stage how many agents need to be in the system, what each agent's role is, and how the agents should interact to get optimal performance out of the group. The aim of the testbed presented here is to investigate which kinds of multi-agent systems could be developed to solve ranges of problems, avoiding the need to reorganize the agents from scratch for each task. The agent organization process explored here is based on the agents' knowledge, and not on their tasks. This opens up a new approach for distributed artificial intelligence designers to have their domain organized before the allocation of tasks among agents. These kinds of organizations should be more robust for solving different problems related to the same knowledge. We define information oriented domains for that purpose. An evolutionary approach to the design of a multi-agent system is suggested. Our model is based on a cellular automaton whose rules of dynamics induce the formation of an organization of agents. Patterns of organization obtained empirically are presented. Our knowledge-based organization approach is analyzed both from theoretical and practical perspectives     

The RETSINA MAS Infrastructure

RETSINA is an implemented Multi-Agent System infrastructure that has been developed for several years and applied in many domains ranging from financial portfolio management to logistic planning. In this paper, we distill from our experience in developing MASs to clearly define a generic MAS infrastructure as the domain independent and reusable substratum that supports the agents' social interactions. In addition, we show that the MAS infrastructure imposes requirements on an individual agent if the agent is to be a member of a MAS and take advantage of various components of the MAS infrastructure. Although agents are expected to enter a MAS and seamlessly and effortlessly interact with the agents in the MAS infrastructure, the current state of the art demands agents to be programmed with the knowledge of what infrastructure they will utilize, and what are various fall-back and recovery mechanisms that the infrastructure provides. By providing an abstract MAS infrastructure model and a concrete implemented instance of the model, RETSINA, we contribute towards the development of principles and practice to make the MAS infrastructure invisible and ubiquitous to the interacting agents.