Radical Agent-Based Approach for Intelligence AnalysisShahram Rahimi, Henry Hexmoor, Bidyut Gupta, Department of Computer Science, Southern Illinois University,

Carbondale, IL 62901, {rahimi, hexmoor, bidyut}@cs.siu.edu

Abstract-This paper presents a novel agent-based individuals from different communities of interestframework as a decision aid tool for intelligence analysis. represented by agents.This technology extends net-centric information processingand abstraction as well as fusion and multi-source Owing to their flexibility and dynamic nature, intelligentintegration strategies. Our information agents traverse and agents are widely used as an interface between users and themediate disparate ontologies in different formats providing a Internet applications. For example, Bollacker utilized anfoundation for semantic interoperability. The presented agent that assists the user in searching for scientificsystem provides knowledge discovery by accessing a large literatures [3], Ackerman developed I-DIAG agent systemnumber of information sources in a particular domain and to refine group collective discussions to be more condensedorganizing them into a network of information agents. Each and strong knowledge [1], and Lieberman employed anagent provides expertise on a specific topic by drawing on agent for helping users browse the Internet by retrievingrelevant information from other information agents in relevant documents according to users' browsing activitiesrelated knowledge domains. Unique advantages include net- [13]. Other attempts such as an information agent calledcentric scalability, principled information assurance, as well SurfAgent to automatically recommend relevant documentsas ground breaking knowledge discovery in service of to users based on their profiles [18], or an agent-basedintelligence analysis. brokering facilitation between users and various information

resources utilize agents to assist users in retrieving1. INTRODUCTION information [17].

There are numerous sources that outline requirements and More recently, Lesser's agent-based information gatheringspecifications for intelligence analysis (IA) decision aid research resulted in the BIG agent architecture (resource-tools [2, 6, 14]. Agent orientation is both a natural metaphor bounded information gathering) [12]. BIG integrates afor intelligence analysis and an obvious IA panacea. The number of Al technologies, including a real-time plannernotion of agents embodies the action orientation we espouse and scheduler, a task modeling tool, and an informationin the Western culture [16]. Most intuitively, the functions extraction/understanding component [5, 8]. As anotherof intelligence analysis connote performing mental actions example, BODHI [11] provides a framework for collectiveof association, correlation, and synthesis, and inference. IA data mining tasks on heterogeneous data sources. Agents areproducts traditionally yield iconic and acting entities, i.e., distributed to local systems to perform data miningagents, such as the AlQaeda. The most notable decision aid processes, and a centralized coordinator agent is responsiblesystems are the biographical generator Progenie [7] and the for managing communication and data mining processesrole playing game ELICIT [9]. ELICIT allowed human among agents.analysts to exchange information and produced offline,empirical validations for group work and collaboration Furthermore, a formal abstract of the distributed multi-agentissues. Our main objective is taking up online monitoring, system is found in dMARS, which is a successor ofanalysis, and proxy function for agents to augment human procedure reasoning system (PRS) [19]. dMARS is anduties. example of a Belief-Desire-Intention (BDI) approach, which

contains four key structures: beliefs, desires, intentions, andAn effective knowledge discovery mechanism for IA should plan library. Beliefs correspond to the information related toprovide the foundation for a rich "knowledge space" the world, Desires represent the goal of the tasks, Intentionsconstructed on top of the basic Internet "data layer." This are chosen desires and will be attempted to be satisfied untilknowledge layer should be composed of value-added either fulfilled or unachievable, and Plan Library specifiesservices that process and offer abstracted information and the courses of action to achieve intentions. Agents in theknowledge rather than returning documents (similar to most dMARS system observe the environmental state, generatecurrent web search engines). possible desires from intentions, and select sequences of

plans to achieve these desires.To recapitulate, traditional agent-based IA systems havelacked netcentric tenets of facilitating individuals to be These approaches are limited to a particular task and are notdisparate in location and in domain. We aim to provide a designed (or scalable enough) to be expanded to a generalsystem that permits collaborations across and within knowledge discovery system. On the other hand, today's

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most advanced multi-agent approaches modestly aim for with access to many different types of informationfragmented information gathering. These architectures do resources.not systematically provide developed knowledge on a targetdomain that includes all the necessary components to fulfill Figure 1 provides an example of a logistic planninga query for intelligence analysis. Furthermore, they lack the information network of information agents. There arecapability to categorize available information and provide several points to note about this network that relate to themechanisms to deal with different data representations on autonomy of the agents. First, each agent may choose tothe WWW. integrate only those parts of the ontology of its information

sources necessary for the task that it is designed for. For2. NETWORK OF INFORMATION AGENTS - example, the transportation-agent may have a fairly

THE GENERAL MODEL complete integration of the sea, land and air-agents, whilethe logistics_planning_agent may draw on only parts of the

We believe that a promising approach to distributed knowledge of the weather and geographic_agents. Secondly,knowledge discovery is to access the large number of we may need to build new agents if we cannot find aninformation resources by organizing them into a network of existing one that contains all the information needed. Forinformation agents, as described by Knoblock [31]. The example, if the geographic_agent does not include somegoal of each information agent is to provide information and particular geopolitical facts required by theexpertise on a specific topic by drawing on relevant logistics_planning_agent, the latter may obtain them directlyabstracted information from other information agents. from the geopolitical information agent. However, if much

of the information is not represented, an alternativegeographic agent would need to be constructed and linked.

og st1c Thirdly, the network forms a directed acyclic graph rathera ent~ ~ than a tree because a particular agent may provide

z Weather 5^teographicA information to other agents that focus on different aspects ofagent aget

,its expertise (i.e., when the port agent is accessed by thentagenstteopolii a geopolitical, air and sea-agents respectively). Nevertheless,

age n Pt) information cycles should be avoided; otherwise, a query may loopendlessly without finding an agent that can actually addressit.

Land t ea Air |tagent t gent ) gent )|

3. THE SYSTEM ARCHITECTURE - DETAILEDPort APPROACH

agent ) > t agent Ja/ Harbor) >The network of information gathering agents is developed

e Atcepository on an autonomous administrative infrastructure (AAI). Forrepository Fleet this structure, the initial framework design incorporates the

Ilantic European Pacific Atlantic use of mobile request agent (MRA), mobile supportingAtlantic f uropea t Pali tant c

Data Repositories Seaport Harbor Seport SeaPort agent (MSA), user interface agent (UIA), information. repositr repository rpsitor epoty, ine f c

manager agent (IMA), and agent administrator (AA).

Figure 1 - Network of information gathering agents as Autonomous Administrative Network of informationUser with fixed Infrastructure gathering agentsviewed by Knoblock network connection

Similar to the way current information resources are __|_Xindependently constructed, information agents are Userwt InfornaAt

developed and maintained separately. They draw on other portable deviceinformation agents and data repositories to provide a newinformation source that others can build upon in turn. Eachinformation agent is another information source, but obieRequest Agentprovides an abstraction of the other available informationresources. An existing information source can be turned into Figure 2 - General infrastructure of the completea simple information agent by building a wrapper that knowledge discovery systemallows it to conform to the conventions of the organization.A class of wrapper agents should be built for any given type To understand how the autonomous administrativeof information resources (e.g., imagery, graphics, text, infrastructure works, let us consider a simple scenarioformatted text, video and audio, etc.) By simplifying the depicted in Figure 2. A user initiates a query by contactingindividual agents to handle one underlying format, it is either a UJA (for users with a fixed connection) or a MRApossible to scale the agent system into networks of agents (for users with a portable device). The MRA is a mobile

agent located on the portable device, which migrates to the

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host of MSA and provides it with the user request. The Figure 3 illustrates a fragment of the domain model of theMRA and MSA compensate for the limited capabilities of sea-agent that belongs to the organization of Figure 1. Themobile devices and provide an entry point for users with nodes represent concepts (i.e., classes of objects), the thickmobile devices to submit requests without the need of high arrows represent sub-correspondences (i.e., subclasscommunication traffics. Next, the MSA delegates the relationships), and the thin arrows represent roles amongrequest to an IMA (there may be several agents of this type concepts (i.e., relationships between classes). Somein AAI for multiple concurrent requests), which in turn concepts that specify the range of roles have been left out ofcontacts the appropriate information agent in the network by the figure for clarity. Some are simple types, such as stringsreferring to its directory of domain models (described or numbers (such as ship-name), while others are definedbelow). The information agent uses its network, cooperating concepts (such as geoloc-code).with other agents in the network (as was described inprevious section), to construct and send the real-time result

cc hcetp ga

back to the IMA. The IMA applies extra formatting of the rLcto

information and transmits it back to the MSA or UIA to begelcodpresented to the user. In the case of portable device users, hpnmMSA provides MRA with functionalities to search and port-na POrAGooCdretrieve the requests back to the portable device. The AgentdokdaAdministrator (AA) in AAI is used for maintaininginformation agents and their networks. Through AA, the Cobt Trnprt Saort Chhaelanneladministrator of the system can generate or modify Si

information agents and links among agents to change thechredpttopology of the system. The administrator can also introduce ammo-capacity soaecpct

new application domains to the system using this agent.an lvto

A similar administrative infrastructure for a multi-agentsystem for geospatial information gathering and integration

Fgr rgeto h oanMdlo h

was successfully designed and developed by one of the Fgr rgeto h oanMdlo h e gnauthors among others, funded by the DoD-NIMA-NURI I---------grant [15] Domain model

ofSea Agent G~graphc-Location

The Knowledge of an Information AgentEach information agent is specialized to a single application range vehicletype lc-o'

domain and provides access to the available informationpotnmsources within that domain. Each agent contains the vhicletype ~m

and models of the other agents that can provide relevant e po......

information-its information source models. The domain er

model is an ontology that represents the domain of interest flotin-crnes (iherted)

for the agent and contains the terminologies for agents to/ j .interact with each other. The information source models ge crane

describe both the content of the information sources and ILA 1y,their relationship to the domain. These models do not need Seaport~

to contain a complete description of other agents, but rather naefloating-craneiftmonly those portions that are directly relevant [29]. A modelr-----------------------1 -----------------------

provides a semantic description of the domain, which is SeaAgent's SeaAgent'sused extensivelyforprocessing queries. i ~~~~~~~~~~~~~Information source model of Information source model of

extensively processing queries. ~~NavalAgent HarborAgent

Each information source model has two main parts. First,there is the description of the contents of the information Figure 4 - The Relationship between an Information Sourcesources. This is comprised of the concepts of interest in Model and a Domain Model (in the Sea Agent)terms of the ontology available in other information sources.The terms in the ontology provide the language that is Figure 4 demonstrate how an information source is modelednecessary to communicate information sources. Second, the and how it is related to the domain model. All of the

Thus, if the user (of the sea_agent) requests all seaports, that The Query Mechanisminformation can be retrieved from the Harbor concept of the Whenever an information request is issued, a list of agentsharbor-agent. Note that the domain model may include that could be related to the subject of the query is producedrelationships that involve concepts coming from different by an information manager agent (described above).agents (like the role docked-at of the ship concept) but are Information agents in this list will initially query theirnot explicitly present in any one information source. knowledge bases, which are ontological documents to

retrieve information. Agents use query languages such asCreating ontologies for the information agents to representtheir domain knowledge and inf osRDQL [20] and SPARQL [21] to retrieve information fromthei doainknowedg an infrmaionsoure kowldge their ontological bases. A RDQL query iS similar to a SQL

is fundamental as these ontologies serve as repositories forthe agents. Web Ontological Language (OWL) [23] is an query with fields for documents from where information isontological language that is utilized in our system. Editors to be retrieved, conditions that need to be matched for thesuch as SWOOP [24] may be used for OWL to create query and restriction that should be considered. Figure 6

ontologies as well as to write queries to retrieve information shows the syntax of an RDQL query similar to what is usedfrom them. Figure 5 is a screenshot of an OWL class tree in our system.created for the system using SWOOP. SELECT variable

FROM document (optional may also beassociated by

the execution environment)|2I~g _d~~ )=/Th:i /S|Ag rtDM#Ship WHERE condition

~ C~d ~ Ab ~tSy~t RDFI~L T~tI~ AND RestrictionOWYL:-c-lass: abiEn

SubeSibl& ::Vbhicle Figure 6 - A sample syntax of an RDQL queryueS edcladdpof ( i

Add Gd _ LII ehI2 If the required information cannot be found by a requestedg ShW Tfpt fE QNan N k i sP==GT_ iS _ information agent, it sends information requests down to the

owlThibg ____A_Mb__ t__1*_@ArmbCapacity d6ck6edAt (a agents lower in the hierarchy. In our system, theGeographicLbWtiboal communication among the agents is done using the,LL~~ 80apbrt Knowledge Query and Manipulation Language (KQML)

i L~ 1rA66dVatqraSe r Annibtationsa 'KnnrneSea?brta e 5[22]. Figure 7 presents a sample KQML query generated by@p G e.oloC:ode.* @P4rtNa6e*Intsrse£tlonefu ( a weather agent and sent to a temperature agent asking for

Sthi66Capbity unio of. ki the current temperature value at Carbondale Illinois.''6;hbatshi Oie- ( Whenever an ambiguous response or an unknown response

vehi6 eType EisuYrle6ttb: ( iS received by an agent as a reply, mediation is required.COnigi!edm6tof. (Ontology Mediation is discussed in the next subsection.

L.6k.~ J'Ii dEEii2[<HIi <27}gg12fffz Ask about

: Content "(Temparature,Carbondale)"

Figure 5 - A snapshot of SWOOP ontology development * Language OWL-DL:Ontology Weather Ontology

using editor : Reply-with "CurrentTemparature": Sender Weather Agent

In our system, OWL is chosen over some of the other : Receiver Temparature Agentexisting languages such as RDF (Resource DescriptionFramework) [4], OIL (Ontology Interchange Language) and Figure 7 - A sample KQML queryDAML (Darpa Agent MarkUp Language) [25]. OWL wasselected mainly because of its comprehensiveness. Ontology MediationFurthermore, every OWL document is also an RDFdocument. As such OWL provides the majority of the As systems such as the one described in the precedingfeatures provided by these other languages while adding sections grow and expand, communication becomes morenew tags to express relations such as transitivity, difficult. What begins as a small, controlled experimentequivalence, inverse etc. OWL has three sub-languages that becomes a heterogeneous chaos of languages and dialectsare OWL-Lite, OWL-DL and OWL-Full. While OWL-Full (described here as ontologies) as newer and different agentsprovides high expressiveness of concepts with a large are added to the system. Rather than requiring all novicelanguage vocabulary, OWL-DL supports automated agents to be backwards compatible with existing agents,reasoning as it is based on Description Logic. Since there needs to be some method of allowing agents withautomated reasoning is crucial in our system OWL-DL was different ontologies to co-exist and interact. Ontologicalselected. Reasoners such as Pellet [26] and Racer [27] that mediation is the ultimate goal for these systems [30].work with OWL-DL have been evaluated. Currently, Pelletis utilized; however, the system architecture and design To develop the mediation algorithm, KQML has beenallow us to alter this choice if needed. utilized as the agent communication language of the system.

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KQML is applied by the agents to communicate with one The proposed system models the above relationship byanother and with the mediation agent for the purpose of using networks of intelligent agents and distributing theontology mediation and to resolve ambiguity. knowledge discovery process, on a particular domain,

among multiple agents responsible for different sub-The mediation agent is actually another information agent domains.which is a higher level agent in the hierarchy compared tothe mediated agents (i.e., in Figure 1, see agent could be a This work has significant scientifical and educationalmediation agent for a naval agent and a harbor agent). To contributions. The presented architecture can be utilized byanswer to a mediation request, a mediation agent first uses a homeland security as a supporting tool for knowledgevoting algorithm for arbitration. It initially sends a send- discovery for the purpose of intelligent analysis. Theinformation-agents-list message to the information manager proposed model will be fully compatible with our intelligentagent (described earlier). Other than the task described database agents for geospatial knowledge collection andabove, information manager agent performs two tasks. It integration architecture [15], previously funded by a NIMA-performs ontological verification and consistency checking NURI in 1999. We are using our previously designedand maintains a list of information agents and brief geospatial knowledge collection and integration system toinformation about their ontologies so that when required it provide our information agents with vector map informationcan provide a directory of agents capable of replying to a through a wrapper agent.particular query. When the list arrives, a multicast messageis sent by the mediation agent to all the agents having An additional motivation for this work is to provide aninformation about the subject of the mediation. Using a architecture that will allow different organizations related tosimple counter, votes are collected for the different homeland security to have the technological capability forinterpretations and the interpretation receiving higher than a providing information visualization tools such as Starlightthreshold number of votes (currently 66%) is selected as the (developed by Pacific Northwest National Laboratory [28])correct interpretation. with real-time domain-based information.

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