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Contact us at GWENetwork jhu. Connect, Inspire, Empower. Jamie Spangler. As a Hopkins alumna, Dr. Spangler recently joined the Department of Biomedical Engineering and is interested in engineering antibody-based molecules that reshape immune cell behavior for targeted treatment of cancer, infectious diseases, and autoimmune disorders.

Coffee and snacks will be provided! RSVP 's are appreciated. Scientific American special issue: "It's not a women's issue" click here to download. Our Story GWEN came about as we began to realize how poorly female graduate are connected and noticed the lack of support structure at the graduate level. After a few discussions, we came up with a plan: Connect, inspire, and empower women by building a network.

Moreover, the user entry-point for the Web usually is a browser application. Thus agents should be integrated with browsers to support user access to online information and functionalities. These issues are not directly addressed by the existing multi-agent development platforms e.

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These platforms were created for the development of closed agent systems, i. If agents are to keep their promise as a technology for semantic Web application development, it makes no sense to have two disjoint concepts: the agent container and the Web. The aim of this paper is to present SemantiCore, an agent infrastructure that integrates the richer semantics of the Semantic Web to the implementation of agent systems. SemantiCore is a framework that provides an abstraction layer for agent-oriented application development for the Semantic Web. It can be integrated with the current web infrastructure extending its computational capabilities to allow agents to process semantic content while the user is navigating on annotated web pages.

This paper is structured as follows. In section 2, we give an overview of the role of software agents in the Semantic Web. From this overview, we show that agents are suitable to develop Semantic Web applications.

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Section 3 introduces the SemantiCore framework. It describes the agents the framework provides, the semantic agent lifecycle and its components. Section 4 illustrates how the framework is successfully exploited in a benchmark Semantic Web application. Section 5 shows some related work and, finally, we draw conclusions and look at challenges for future research on environments in section 6. Semantic Web technologies seam to be way to provide the semantic integration between data and processes across systems that can be owned by different enterprises [2].

This technology is still in progress [3, 8], for instance the definition of languages for expressing the semantics of the Web is not mature yet [14]. However different the powerful synergism between agents and Semantic Web could be very promising [13, 18] and some efforts have been made in order to define ontology models and develop tools suitable for agents aiming at being truly semantic-aware agents.

Therefore a key component in the semantics-rich approach is the ontology. An ontology is the specification of a conceptualization, that is it is the formal, agreed vocabulary whose terms are used in the construction of a semantic system. An ontology is a conceptualization of an application domain in a human-understandable and machine-readable form, and typically comprises the classes of entities, relations between entities and the axioms which apply to the entities which exist in that domain.

The Semantic Web will not be primarily comprised of nice organized ontologies that have been carefully constructed by experts; instead, it will be a complex web of semantics ruled by the same sort of havoc that currently rules the rest of the web. Rather than a few large, complex, consistent ontologies, shared by great numbers of users, the Web will be composed of a great number of small ontological components [7]. Information will be exchanged between applications, allowing computer programs to collect and process web content, and to exchange information with each other.

An agent is a software system that is i situated in some environment, ii capable of autonomous actions in order to meet its objectives and iii capable of communicating with other agents [20]. Therefore agents are situated systems and the environment should provide agents the essential information to work. Thus the highly semantic infrastructure of the Semantic Web will make agent-based computing much more practical [7].

However, one challenge for Agent-oriented Software Engineering AOSE is to provide an affordable way to introduce the design and implementation of intelligent knowledge-based behavior into mainstream Software Engineering [24]. The issue is not simply provide a method for integrating ontologies and agents but providing an implementation framework that is not simply creating dedicated isolated containers for agents to execute but rather use the Semantic Web and all its features to deploy open and flexible software systems composed of knowledge-aware agents.

MAS development platforms usually focus on the distribution issues such as concurrency control, message passing, environment management and internal agent architecture implementation. SemantiCore was developed to fulfill the gap the existing MAS platforms have for creating open MAS on the Web capable of processing semantic content annotated in Web pages.

SemantiCore was developed to be integrated to web servers and web browsers in order to create Semantic Domains where software agents live thus extending the Web without interfering with its current structure. Thus, a Semantic Domain can be defined as an extension of the current web domain that provides an environment for agents capable to process ontologies to run. This hybrid computational model allows agents to coordinate their actions to other agents "living" on the web to achieve a common goal.


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Figure 1 illustrates how SemantiCore modifies the current web architecture providing a software layer that processes semantic content using intelligent agents. Since SemantiCore is based on the FIPA Reference Model [6] it provides internal domain messages routing, external domain messages routing, services directory facilities, agent registration services and agent execution contexts. These characteristics are distributed among different domain agents such as the Environment Manager, the Domain Controller and the Service Directory.

The Environment manager is an agent that captures information on the web browser or the web server, depending if the Semantic Domain is integrated to a browser or a web server, and passes this information to the Semantic Domain. It also passes back the information resulted from the agents processing to the web. When the user navigate in a Semanticore enabled domain the server must send response variable in the HTTP protocol to signal the presence and the identifier of the Semantic Domain. The Semantic Domain attached to the browser is then able to connect the Semantic Domain attached to the server allowing agent to communicate directly through the environment.

The Domain Controller has, among other capabilities, the potential to connect different Semantic Domains depending on the interests of the agents running on these domains. It is responsible to detect and connect different Semantic Domains allowing the message passing and routing to the appropriate receiver. Social regulations and agent specific regulations must be implemented directly by the developer and do not use the supervisory feature provided by the Domain Controller.

Other Semantic Domains features include the support of native code migration, which allows the creation of mobile agents and the definition of different channels for message exchange. Migration can be done simply by a method invocation which must indicate the location to move. Control messages among management agents and components of an agent are sent in a separate control channel while messages exchanged by application agents are sent using a data channel.

SemantiCore agents extend the Semantic Agent class figure 2. The agent starts its execution by calling the setup method. During setup, the developer may create sensors, facts, rules, effectors, actions, action plans, and goals for the agent. All these structures are created using SemantiCore classes and form the SemantiCore reference model for the Semantic Agent. The Semantic Agent class has some in-built methods to add all the elements an agent may have in SemantiCore. The addSensor method enables the agent to add a previous defined TestSensor in its definitions line Sensors are created extending the Sensor class.

Agents may have different types of sensors working simultaneously depending on the kind of messages they want to capture, e. The setDecisionEngine line 07 method is used to define the inference engine to be used for manipulating ontologies. This method allows the creation of agents with different decision making methods, e. During setup it is also possible to define facts, rules and actions. SimpleFact, FunctionBasedFact and CompositeFact classes are used to create different patterns of facts that may be associated to the decision making method or that may be part of a rule this method must take into account.

An action extends a FunctionBasedFact so it may be automatically started from the decision making method in this case the inference engine. The action has some arguments which are used to pass the data sensed in the environment or discovered during the decision making. The developer has complete access to the agent and its execution context such as variables in each action.

Setup is only executed once when the agent is created in the environment.

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Once an agent has started, it basically performs 4 operations in loop during its execution: senses the environment, decides in accordance to the information sensed, executes the actions depending on the decisions made and publishes information back in the environment. This lifecycle is automatically managed by SemantiCore. The four basic lifecycle operations are encapsulated into components: sensorial, decision, executor and effector.

These specialized components allow better maintenance, extensibility and organization of code. Figure 4 shows the agent component architecture.

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The Sensorial component manages all the different sensors an agent has, selecting one of them dynamically according to the kind of message received from the environment. This component generates a list of objects that are transmitted to other components depending on the communication links mapped. The basic SemantiCore distribution maps the sensorial output to the decision input. The Decision component handles the rules and facts that form the mental model of an agent. It also manages the decision making mechanism used to decide over the data received by sensorial output. The facts and rules have specific hotspots for their representation in different formats.

The output of the decision making is a list of actions to be performed. Semantic agents have all information sensed stored in their data contexts. This information is formed by individuals in the ontologies processed and it is made available to all running actions by the execution component. Actions may need to send messages to other agents in the Semantic Domain as a part of its processing. The encapsulation of these messages in different structures and their transmission in the environment is the responsibility of the Effector component.

The effector component selects dynamically the effector that must be used to codify the message that will be transmitted. This is done by a dynamic instantiation depending on the type of effector needed to send a message.


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  4. When an agent is created, the developer may indicate the types of effectors the agent will work with. Component organization, different hotspots instantiation, different formats and native OWL processing contribute to differentiate SemantiCore from other MAS platforms. SemantiCore enables the agent component distribution over machines. This unique feature allows SemantiCore to adapt to particular Semantic Web performance requirements.

    For example, if the Decision component demands higher processing capability than the Sensorial component, it may be distributed in a high performance hardware. Figure 5 shows the result of a performance test executed on an application developed see section 4 using SemantiCore.

    The graphics show the performance upgrade distribution. The performance upgrade continues to be relevant as the number of agents in the domain grows although it decreases in absolute values. This is partially explained by the overhead produced by multiple threads management on a single machine versus the communication overhead among them.

    The test repeated 5 times was run using four Pentium IV, 3. Each machine had a part of the same SemantiCore domain where 4 agents were executed. One of these agents had the decision component centralized in the first testing set and distributed in the second one. Another relevant characteristic is SemantiCore ontology-based agent representation as shown in figure 6. This representation maps all the elements a Semantic Agent has, allowing it to reason about itself and about other agents in the domain. This representation also allows browsers to capture agent definitions on Semantic enabled web sites and execute the agent based on the representation.

    It is important to consider that the ontology does not have only concept definition, but also instances definition enabling the agent instantiation. The content of the agent action is coded using the Java language embedded in the instance ontology definition.

    Other elements use the ontology properties to define their instances. This section presents a case study adapted from the classical Semantic Web example published in [2]. The adaptation aimed to better specify the problem in order to build an agent-based solution for this problem.

    The example shows two brothers, Pete and Lucy, trying to schedule physiotherapy sessions for their mother, who will be called Marie for explanations purposes. Pete and Lucy have personal digital assistants Semantic Agents, named AgPete and AgLucy respectively to execute certain specialized tasks. The discussion about how they interface with their users is out of the scope of this work.

    The first interaction occurs when Lucy decides to give the scheduling task to her agent. These agents execute on different machines and can and may be on a single domain or multiple domains since SemantiCore abstracts the distribution issues.