host of templates are available, playbook rules decide which template provides most utility to the application. The rules or the basis governing selection (relating ...
Ontological Specification of Performance Models for Multi-agent Systems Jyotirmaya Nanda, Nathan Gnanasambandam, Soundar R.T. Kumara, Timothy W. Simpson The Pennsylvania State University University Park, PA, 16802 Abstract Large-scale multi-agent systems (MAS) utilize performance models for their optimization. These models could be queueing network models such as Jackson or BCMP networks that can capture the flow of tasks through the network of agents and estimate quantities such as response time and queue lengths. The selection of the model and subsequent analysis of the system is often done offline. We propose a generic ontological framework to capture the semantics of various performance models to facilitate online analysis of MAS with minimal human interaction. Through the framework, we illustrate the specification of queueing models used for MAS performance optimization. Keywords: Multi-Agent Systems, Performance Models, Semantic Web 1 Introduction The use of performance models for large-scale multi-agent systems (MAS) is prevalent both for offline analysis as well as real-time control, but the utilization of these models is often times facilitated by human-in-the-loop procedures such as model development, testing, refinement and integration. Although, human interaction is necessary in model development and initial testing, subsequent steps leading to the utilization of these models in various conditions can be automated. For this automation, detailed specifications of various model templates and how particular instances can be derived for use with an existing real-time system is imperative. In addition to model specifications, Technical Specifications (or TechSpecs) relating to the capabilities, input-outputs, control actions, behavioral rules and play-books of the system’s component’s are required for applying the models in an appropriate way. To this end, the goal in this paper is to employ a generic ontological framework for TechSpecs and model specifications to a multi-agent system (called CPE) so that online analysis is facilitated. The Semantic Web was envisioned by World Wide Web Consortium (W3C)1 as an extension of the current web where much of the unstructured or semi-structured information can be treated in a machine processable way and ultimately create knowledgeable systems with various specialized reasoning services systems [1]. Ontologies developed for many fields to establish common vocabularies and capture domain knowledge have proven to be an advantageous paradigm over recent years [2]. The high payoff of saved effort due to reuse of pre-existing knowledge captured in ontologies is discussed by Gennari et al. [3]. The use of ontologies to capture the semantics of queueing models, play-books, and real-time information to perform automated real-time analysis of a continuous, planning and execution (CPE) MAS will yield similar benefits. We use the web ontology language (OWL-DL) to capture the semantics of the performance models and the semantics of the system’s technical specifications in a generic way. Furthermore, graph-based querying capabilities of OWL are used to assist the system search for plausible control actions. The model specification and system’s TechSpecs of the system and are captured in a systematic way using ontologies which is later used in the analysis of MAS. 2 Background and Related Work In this section, we discuss some MAS performance models and provide some background about ontologies. 2.1 Large-Scale Multi-agent Systems and Performance Models In large-scale MAS applications, performance estimation and modeling can be a formidable task as illustrated by [4] in the UltraLog2 context. UltraLog, built on Cougaar3, uses for heuristic control a host of architectural features such 1
http://www.w3.org http://dtsn.darpa.mil/ixo/ixo_FeatureDetail.asp?id=61 3 http://www.cougaar.org/ 2
as operating modes, conditions, and plays and play-books as described in Ref.[5]. Helsinger, et al. [6] incorporate the aforementioned features into their closed-loop heuristic framework that balances the different dimensions of system survivability through targeted defense mechanisms, trade-offs and layered control actions. The importance of high-level, system specifications (interchangeably called TechSpecs or MAS Specifications) has been emphasized in many places such as [5,7,8]. These specifications contain component-wise, static input/output behavior, operating requirements and control actions of agents along with domain measures of performance and computation methodologies [8,9]. Also, queueing network-based methodologies for offline and design-time performance evaluation have been validated in Refs. [8,10]. Building on these ideas, we propose ontological representations for queueing-based performance models that assist in real-time performance prediction. 2.1 Ontology and the Semantic Web An ontology consists of a set of concepts, axioms, and relationships that describes a domain of interest. These concepts and relationships between them are usually implemented as classes, relations, properties, attributes, and values (of the properties/attributes) [11]. Ontology can be defined as [12]: “An ontology is a formal, explicit specification of a shared conceptualization.” Attributes “formal” and “explicit” enable the automatic machine-based interpretation of the conceptualization; “shared” enables the sharing, combination, and integrated use of ontological information [13]. Ontologies need a language for semantic representation and reasoning. The W3C's Semantic Web initiative proposes a layered approach to a standard ontology language, OWL [14], which has been used in this paper for capturing ontologies. The Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL comes in three sublanguages - OWL Lite, OWL DL, and OWL Full - to support different levels of expressiveness [15]. OWL [14] is well suited for ontological specification of performance models in agent-based systems by supporting reasoning outside the transaction context, i.e., avoiding a protocol specification to handle standard data format. Also, software tools, irrespective of the subject domain, can provide support for the ontologies. A detailed review of OWL and its applications can be found in Ref [16]. Though performance models are stored in program structures, the semantics (meaning) of the mathematical constructs are almost never stored in an information system. The development of common ontological model templates can help capture the semantics and provide a standard vocabulary for creating and maintaining performance models for agent societies. The performance models are represented as instances of ontological model template classes. The inheritance-based representation using OWL helps in consolidating scattered information in a hierarchical structure and decreases the amount of information needed to describe performance models. 3 Ontological Specification of Performance Models In this section we describe development and application of the ontological model to MAS. 3.1 Method for developing Ontological Model Templates for Agent Based Systems In this paper we have used Formal Concept Analysis (FCA) [17] for structuring the information related to various concepts associated with performance models of agent-based systems. Formal concept analysis, introduced by Wille in 1982 [17], is used for analyzing data and forming semantic structures that are formal abstractions of concepts of human thought. FCA borrows its mathematical foundation from order theory, the theory of complete lattices [18] in particular. After structuring the information using FCA the lattice structure is encoded using OWL. Details of systematically developing ontologies using FCA can be found in Ref. [19]. Ontological Model Templates describe various models for MAS. For example, the Performance Template describes a performance model for MAS. The properties of the Performance Template are based on the concepts from mainly two templates, namely, (1) Math Template and (2) Parameter Template. The methodology allows the extension of the templates in a systematic way, but for illustration purposes, only two templates and several concepts therein are considered for the development of the Performance Template.
The development of the template ontologies is done using PFODM [19]. Table 1 shows the basic cross table that captures the relationship between the properties and concepts of the MathTemplate. For example, the concept {ProbabilityTemplate} has the property {hasDistribution}. Table 1: MathTemplate Cross Table
Based on the properties of the individual concepts, using FCA the concept lattice is formed (see Figure 1). The concept lattice, formed based on the properties, captures the taxonomic relationship between the individual concepts in the MathTemplate.
Figure 1: MathTemplate concept lattice The properties and the taxonomic hierarchy are then encoded using OWL DL. The class hierarchy of the MathTemplate is shown in Figure 2. The ProbabilityTemplate, DataRepresentationTemplate, and SolverTemplate are of type MathTemplate.
Figure 2: OWL Class hierarchy in MathTemplate The Performance Template is made out of the MathTemplate, and IOTemplate. The class attribute pair for the Performance Template is ({PerformanceModel}, {hasInput, hasOutput, hasAssumptions, hasMathOperator, hasDataStructures}). In this OWL class hasInput property is an ObjectTypeProperty of domain Input of IOTemplate.
Figure 3: OWL Class hierarchy in ParameterTemplate
Using this methodology other class hierarchies for concepts of the MAS are developed. These taxonomic class hierarchies are then encoded using OWL. OWL DL, a standard prescribed by W3C, captures both the taxonomic relationship as well as the properties associated with the concepts in a distributed way. 3.2 Application of Ontological Model Templates on Agent Systems We first explain the flow of events in the application of ontological model templates on agent systems. Gauging the performance of the MAS has to do with taking the inputs that the distributed MAS receives and applying some mathematical operations on them. In doing so, the actual values have to be stored in appropriate data structures and/or the computation may require the use of specialized structures such as matrices, stacks or heaps. Besides, the application of a specific type of ontological model template, say analytical queueing network model template, is contingent upon certain assumptions to be satisfied. Once the aforementioned conditions hold true, a certain output can be generated, stored and/or returned to an appropriate agent. The Performance Template shown in Figure 4 is the starting point of performance analysis. It specifies that a certain output can be generated if accompanying attributes are initialized. The output in the case of a Performance Template is a performance measure or quality of service (QoS) such as response time, delay, delay-jitter or loss-probability. The specification mandates that the inputs and outputs be specified according to a Parameter Template (shown in Figure 3), which further drills down the details of input and output parameters. In this case the Input Template and the Output Template are linked in an OWL class hierarchy in Parameter Template. Even within the Parameter Template assumptions may be nested, but these will be particular to the inputs or outputs as the case may be. On the other hand, the Performance Template has an assumptions attributes to account for other model assumptions such has play-book rules which specify such things as “when to use the model template”. The Performance Template also specifies mathematical operators and capabilities, which are necessary during computation. In this way, it notifies the computation module (say, an online analysis agent) that it must have access to several library routines relating to the specified mathematical operations. Several agent-based systems have library routines of mathematical nature built into their architecture or have access to publicly available routines either through web-services or specialized applications (say through a dedicated connection to a Matlab or Arena server). Data structures to hold matrices or equations of various kinds are also necessary to complete the loop in this specification, which is essentially about a computation procedure and the associated requirements. In this way various concepts associated for performance modeling of MAS is captured using OWL.
Figure 4: Mapping concepts between templates 4 Application of Ontological Model Templates on CPE In this section we describe the use of ontological model templates in a MAS. 4.1 Continuous Planning and Execution (CPE) MAS The CPE society comprises of several agents and a world model. Agents in the CPE society such as the brigade (BDE), battalion (BN), company (CPY) and supplier (SUPP) assume a combination of command and control, and customer-supplier roles as required in a military logistics scenario. The world model is an artificial source that provides the agents with external stimuli. Each agent in the society constantly performs one or more of the following tasks: 1) Evaluates its own perception of the world state through local sensors and remote inputs; 2) Performs planning, re-planning, plan reconciliation and plan refinement; 3) Executing plans, either through local actuators or through sending messages to other agents; and 4) Adapting to the environment, e.g. centralizing or decentralizing planning as computational resources permit. Ontologically specifying the aforementioned functional and operational details within the CPE society codifies the information flow within CPE. Such an organization of information
enables the seamless application of performance models for CPE, provided the models are also available in a generic, platform-independent representation. 4.2 Development of Ontological Model Templates of Performance Models for CPE We now explain the ontological model in the context of CPE. While executing its functionality in a distributed fashion, the MAS sends (process or update) tasks to agents in the CPE society. Within the Performance Template the inputs correspond to the rates at which these tasks arrive at various agents as well as their types. Since we are dealing with a performance model that is a queueing network, task arrivals have to correspond to a particular distribution. Hence, the external tasks (tasks that arrive from external sources and not those internally routed) must conform to a Poisson distribution if we are using a Jackson or BCMP network. This constitutes one of the assumptions. Another assumption relating to the type of tasks is that their processing time must either be exponentially or generally distributed for Jackson and BCMP networks respectively. These assumptions are explicitly specified in the Parameter Template (i.e., hasArrivalModel and hasProcessModel) such that they can be verified. The tasks may be routed within the network of agents and this is captured in hasTransitionProbabilites as the probability of transmission from one agent to another. The output of interest in the case of CPE is response time for various task flows emanating from specific agents and ending at others. This is specified in the Output Template, which is part of the Parameter Template. Within the Performance Template, there can be other assumptions that need to be specified. For example, when a host of templates are available, playbook rules decide which template provides most utility to the application. The rules or the basis governing selection (relating to speed or accuracy from prior history) may be specified within hasAssumptions in Performance Template. In the case of Jackson or BCMP networks, two-dimensional matrices, linear equation and their associated solvers are need to compute the response time. This information is made available through hasMathOperators and hasDataStrcutures, which are part of the MathTemplate. As explained before, the Math Template is an OWL repository of several data representation methods and solvers. 4.3 Using Ontological Model Templates for Performance Evaluation of CPE The primary advantage of ontological model templates is that preconceived ontologies stored in OWL can used to capture the semantics of queueing models, play-books, and real-time information to perform automated real-time analysis of a continuous, planning and execution (CPE) MAS. As mentioned earlier, the TechSpecs of the agent society can de dynamically discovered and composed, thereby making the specification of the larger MAS available. This composed TechSpec can be leveraged by a specialized analysis module (say another agent), which has access to the ontological model templates either through static linkage (provided along with agent during deployment) or through web-services. The analysis module will gather the real-time information and play-books from the TechSpecs, and present them to all available model templates to first isolate a suitable candidate for analysis. Since there may be multiple model templates available, distinguishing characteristics must be present. In this paper, we distinguish them through the hasAssumptions attribute that can relate to another OWL hierarchy specifying prior history of speed and accuracy, or what kind of analysis is pertinent. Once a match is identified, performance analysis is performed by making use of the specification described in Section 4.2, and the MAS is informed of the results of the analysis. Based on this larger picture, the MAS is in a better position to control itself. In the work, we have restricted ourselves to modeling performance templates alone; however, the idea is easily extended to other types of models such as those for system reliability. 5 Conclusion and Future Work In this paper, a generic ontological framework is introduced to capture the semantics of some queueing-based performance models to facilitate online analysis of multi-agent systems with minimal human interaction. We use web ontology language (OWL-DL) to capture the semantics of the performance models and the semantics of the system’s technical specifications in a generic way. This would subsequently enable us to use graph-based querying capabilities of OWL to search for plausible control actions. In the proposed ontological framework, we demonstrate the ontological specification of a performance model of a MAS. Specifically, preconceived ontologies stored in OWL are used to capture the semantics of queueing models, play-books, and real-time information to perform automated real-time analysis of a continuous, planning and execution (CPE) MAS. We find that the framework assists in the automated analysis and control of the CPE agent society.
Acknowledgement This work was partially supported by the DARPA UltraLog Grant#: MDA972-1-1-0038. This work was also funded in part by the National Science Foundation under Grant No. IIS-0325402. Any opinions, findings, and conclusions or recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation. References [1] Davies, J., Fensel, D. and Harmelen, F. v., 2003, Towards the Semantic Web: Ontology-Driven Knowledge Management, John Wiley & Sons, West Sussex, UK. [2] van der Vegte, W. F., Kitamura, Y., Mizoguchi, R. and Horváth, I., 2002, "Ontology-based Modeling of Product Functionality and Use - Part 2: Considering Use and Unintended Behavior," EdiPROD Conference, October 10th, Poland, pp. 115-124. [3] Gennari, J. H., Tu, S. W., Rothenfluh, T. E. and Musen, M. A., 1994, "Mapping Domains to Methods in Support of Reuse," International Journal of Human-Computer Studies, 41(3), pp. 399-424. [4] Helsinger, A., Lazarus, R., Wright, W. and Zinky, J., 2003, "Tools and Techniques for Performance Measurement of Large Distributed Multi-agent Systems," Autonomous Agents and Multi-Agent Systems, Melbourne. Australia, [5] Kleinmann, K., Lazarus, R. and Tomlinson, R., 2003, "An Infrastructure for Adaptive Control of Multi-Agent Systems," IEEE Conference on Knowledge-Intensive Multi-Agent Systems, [6] Helsinger, A., Kleinmann, K. and Brinn, M., 2004, "A Framework to Control Emergent Survivability of Multi Agent Systems," Autonomous Agents and Multi-Agent Systems, New York, NY, [7] Jennings, N. R. and Wooldridge, M., 2000, Handbook of Agent Technology, AAAI/MIT Press, [8] Gnanasambandam, N., Lee, S., Gautam, N., Kumara , S. R. T., Peng, W., Manikonda, V., Brinn, M. and Greaves, M., 2004, "Reliable MAS Performance Prediction Using Queueing Models," IEEE Multi-agent Security and Survivabilty Symposium, Philadelphia, PA, USA, pp. 55-64. [9] Cassandra, A., Wells, D., Nodine, M. and Pazandak, P., 2003, "TechSpecs: Content, issues and nomenclature," Telcordia Inc. and OBJS Inc., [10] Gnanasambandam, N., Lee, S., Kumara, S. R. T. and Gautam, N., 2005, "A Framework for Performance Control of Distributed Autonomous Agents," Industrial Engineering Research Conference, Atlanta, GA, USA, [11] Daconta, M. C., Obrst, L. J. and Smith, K. T., 2003, The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management, Wiley Publishing, Inc., Indianapolis, Indiana. [12] Gruber, T. R., 1993, "A Translation Approach to Portable Ontology Specifications," Knowledge Acquisition, 5(2), pp. 199-220. [13] Hyvönen, E., 2001, "The Semantic Web - The New Internet of Meanings," Semantic Web Kick-Off in Finland Vision, Technologies, Research, and Applications, October 2, Helsinki, Finland, Helsinki Institute for Information Technology Publications, pp. 3-26. [14] McGuinness, D. L. and Harmelen, F. v., 2003, "OWL Web Ontology Language Overview," http://www.w3.org/TR/2003/PR-owl-features-20031215/, March 20, 2004, The World Wide Web Consortium Proposed Recommendation. [15] Smith, M. K., Welty, C. and McGuinness, D. L., 2003, "OWL Web Ontology Language Guide," http://www.w3.org/TR/owl-guide/, February 2, 2004, The World Wide Web Consortium - Proposed Recommendation. [16] Nanda, J., Thevenot, H., Simpson, T. W., Kumara , S. R. T. and Shooter, S. B., 2004, "Exploring Semantic Web Technologies for Product Family Modeling," International Design Engineering Technical Conferences and Computers & Information in Engineering Conference, Salt Lake City, Utah, USA, ASME, DETC2004/CIE-57683. [17] Ganter, B. and Wille, R., 1999, Formal Concept Analysis: Mathematical Foundations, First, Springer-Verlag, Heidelberg, Germany. [18] Gratzer, G. A., 1998, General Lattice Theory, Second, Birkhäuser, Boston, MA, US. [19] Nanda, J., Simpson, T. W., Kumara, S. R. T. and Shooter, S. B., 2005, accepted for publication, "Product Family Ontology Development using Formal Concept Analysis and Web Ontology Language," Journal of Computing and Information Science in Engineering.
