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The shift from Web pages to Web services enables program- matic access to the near limitless information on the World Wide Web. Autonomous agents should generate concise answers to complex questions by invoking the right services with the right data. However, traditional methods of programming automated query processing capabilities are inade- quat...
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... services are located at different places on the internet, but each has its own OWL-S service descrip- tion. In Figure 3, a user/client asks MPAC to compose an agent that can get informa- tion about European ships from databases located within North America. The client sends a description of the goal (desired output of 'Ship Report Data') using terms from an OWL ontology to the service locator. ...
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Citations
... For example, effective coordination between estimation and management functions is based on mission needs [22] that come from the essential elements of information (EEIs). Other challenges include ontologies supporting web services [23], machine translation [24], and evaluation [25]. These mission needs are sent to analysts who must forage for data to answer queries, information needs, and mission perspectives. ...
Current analysts are inundated with large volumes of data from which extraction, exploitation, and indexing are required. A future need for next-generation analysts is an appropriate balance between machine analytics from raw data and the ability of the user to interact with information through automation. Many quantitative intelligence tools and techniques have been developed which are examined towards matching analyst opportunities with recent technical trends such as big data, access to information, and visualization. The concepts and techniques summarized are derived from discussions with real analysts, documented trends of technical developments, and methods to engage future analysts with multiintelligence services. For example, qualitative techniques should be matched against physical, cognitive, and contextual quantitative analytics for intelligence reporting. Future trends include enabling knowledge search, collaborative situational sharing, and agile support for empirical decision-making and analytical reasoning.
... For example, effective coordination of estimation and management functions for sensing requires assessment based on mission needs (Blasch, Nagy, et al., 2014). Other challenges include ontologies supporting web services (Czajkowski, et al., 2004), machine translation (Czajkowski, et al., 2006), and evaluation (Costa, et al., 2012). These mission needs are sent to analysts who must forage for data to answer queries, information needs, and mission perspectives. ...
Qualia-based Exploitation of Sensing Technology (QuEST) is an approach to create a cognitive exoskeleton to
improve human-machine decision quality. In this paper, the authors present QuEST-motivated man-machine
information fusion system is presented for situation awareness. User-based situation awareness includes both
elements of external sensory perception and internal cognitive explanation. The authors outline QuEST elements and are used as a reasoning approach to achieve human intelligence amplification (IA) in relation to
data aggregation from machine artificial intelligence (AI). In a use case example for multimedia exploitation,
the QuEST approach enhances enhanced understanding of the man (mind-body cognition) and the machine
(sensor-based reasoning) by establishing a cohesive narrative of situational activities. QuEST tenets of structurally coherent, situated conceptualization, and simulated experience are utilized in organizing multimedia
reports of Video Event Segmentation by Text (VEST).
... For example, effective coordination of estimation and management functions for sensing requires assessment based on mission needs [27]. Other challenges include ontologies supporting web services [28], machine translation [29], and evaluation [30]. These mission needs are sent to analysts who must forage for data to answer queries, information needs, and mission perspectives. ...
Qualia-based Exploitation of Sensing Technology (QuEST) is an approach to create a cognitive exoskeleton to improve human-machine decision quality. In this paper, we discuss the motivation of QuEST as it pertains to man-machine information fusion. Operator-based situation awareness includes both elements of external sensory perception and internal cognitive explanation. We outline QuEST elements and tenets towards a reasoning approach that achieves human intelligence amplification (IA) as opposed to data aggregation from machine artificial intelligence (AI). In a use case example for automatic target exploitation, we showcase the need for enhanced understanding of the man (mind-body cognition) and the machine (sensor-based reasoning) for establishing a cohesive narrative of situational activities.
... Previously, we proposed an adaptive peer-to-peer agent-based approach (APPA) for dynamically deployed sensor networks [9] and an agent-based hierarchical task planning service composition approach using meta-planning concept [10]. It did not deal with the challenges of dynamic situations. ...
In this paper, we describe a multiagent-based adaptive ad-hoc pervasive service architecture. It provides dynamic context-aware service and operation management including semantic service match-making, composition, execution, monitoring, and autonomic recovery. The intended application is decision support in tactical field operations. This domain requires dynamic adaptive composition of services, resources, and assets via ubiquitous and pervasive computing devices connected by mobile wireless peer-to-peer networks. Our goal is to provide on-demand pervasive services to meet users' dynamic situational information needs. Our solution uses distributed peer service composition agents to adaptively weave within and across three abstract service layers, i.e., the agent-based peer service composition agent layer, the agent-based wrapper service layer, and the resource and asset layer.
... Developed by ATL, the Meta-Planning for Agent Composition (MPAC) (Czajkowski, Buczak, and Hofmann 2004) framework automatically composes and executes semantic web services written in OWL-S. MPAC dynamically selects semantic web services based on availability through match-making algorithms similar to those described in (Sycara et al. 2003). ...
Developers have built many eGovernment applications using local ontologies to provide a meta-data description of what their service does, how it works, and how to invoke it. Every day, more of these local ontologies are written and posted on web servers around the world. Consequently, effective bridging of semantic web applications for eGovernment is proving to be a challenge. This paper briefly describes a new technique to bridging disjoint semantic web applications by automatically aligning their ontologies and performing message translation. The challenge is to create a tool to increase efficiency of alignment without reducing accuracy. We demonstrate our solution in an eGovernment scenario of translating driver's license information between two services in different U.S. states.
... Lockheed Martin's Advanced Technology Laboratories (LM ATL) has developed agent technology that offers promising solutions to the problems underlined in SRL. ATL has applied agent technology in more than two dozen projects covering a full range of intelligent systems, including information management for time-sensitive strike, situation awareness for small military units, and execution of user requests entered via spoken language dialogue [4,5,8,19,9,20]. ATL's agents were also used in Navy Fleet Battle Experiments (FBE) as human aiding tools [20]. ...
We present a novel cognitive agent architecture and demonstrate its effectiveness in the Sense and Respond Logistics (SRL) domain. SRL transforms the static, hierarchical architectures of traditional military models into re-configurable networks designed to encourage coordination among small peer units. Multi-agent systems are ideal for SRL because they can provide valuable automation and decision support from low-level control to high-level information synchronization. In particular, agents can be aware of and adapt to changes in the environment that may affect control and decision making. Our architecture, the Engine for Composable Logical Agents with Intuitive Reorganization (ECLAIR) is a framework for enabling rapid development of coherent agent systems that adapt to their environment once deployed. ECLAIR is based on cognitive theories for motivation and adaptation, including Piaget's Assimilation and Accommodation [21] and Damasio's Somatic Marker Hypothesis [6]. To demonstrate our preliminary work, we implemented a simple simulation environment where our agents handle the ordering and delivery of supplies among operational and supply units in several scenarios requiring adaptation of default behavior.
... Lockheed Martin's Advanced Technology Laboratories (LM ATL) has developed agent technology that offers promising solutions to the problems underlined in SRL. ATL has applied agent technology in more than two dozen projects covering a full range of intelligent systems, including information management for time-sensitive strike, situation awareness for small military units, and execution of user requests entered via spoken language dialogue [4,5,8,19,9,20]. ATL's agents were also used in Navy Fleet Battle Experiments (FBE) as human aiding tools [20]. ...
We present a novel cognitive agent architecture and demonstrate its effectiveness in the Sense and Respond Lo-gistics (SRL) domain. Effective applications to support SRL must anticipate and adapt to emerging situations and other dynamic military operations. SRL transforms the static, hi-erarchical architectures of traditional military models into re-configurable networks designed to encourage coordina-tion among small peer units. Multi-agent systems are ideal for SRL because they can provide valuable automation and decision support from low-level control to high-level infor-mation synchronization. In particular, agents can be aware of and adapt to changes in the environment that may af-fect control and decision making. Our architecture, the En-gine for Composable Logical Agents with Intuitive Reorga-nization (ECLAIR) is based on cognitive theories for mo-tivation and adaptation [6, 13, 21]. Agents respond to ex-ternal stimuli and internal perception of wellbeing. In nor-mal situations they act logically, using plans, or workflows, when there is a known strategy to accomplish a task. How-ever, when quick reaction is needed, motivation for action is intuitive or reflexive. Adaptation using machine learn-ing techniques improves both logical and reflexive behav-iors in ECLAIR. To demonstrate and evaluate our approach, we implemented a small simulation environment where our agents handle the ordering and delivery of supplies among operational and supply units in several scenarios requiring adaptation of default behavior.
... Discovery of what a sensor can do and its constraints is guided by descriptions written in the Web Ontology Language (OWL) [21]. Such a process, called "match-making" [23], has been proven successful in our agent-based integration efforts, first starting in [8]. When applying match-making to a sensor network, service agents can automatically discover one another and ask each other to perform tasks peer-to-peer. ...
We present an agent-based, adaptive peer-to-peer, hybrid architectural approach for sensor networks to address some of the challenges and needs presented in net-centric, field-deployed, or soldier-walk-and-drop ad-hoc sensor networks. The proposed Adaptive Peer-to-Peer Agent Architecture (APPA) combines the benefits of adaptive peer-to-peer, agent-based, and service-oriented architectures to address the survivability, robustness, performance, flexibility, scalability, and support of federated services across sensor groups in sensor networks. The APPA enables dynamic self-configuration of independent, but cooperating agents. These agents work as service proxies for sensors to proactively use each others' agent context information to cooperate and coordinate sensors for task allocations and task or agent migration using its mobile and agent architecture. The intended goal is to meet some of the unique challenges as anticipated in a dynamic, small-team echelon in battlefield. The purpose is to provide context-driven situational awareness to help command and control commanders in decision-making. The APPA is built on Lockheed Martin Advanced Technology Laboratories' previously proven mobile agent technology and extended with peer-to-peer capability, service-oriented architecture, and recent advances in market-based team formation and Distributed Constraint Optimization Problem (DCOP) [17, 18, 20, 22]. adaptive peer-to-peer agent-based architecture, SOA architecture, market-oriented adaptive task allocation for resource sharing, grouping, and teaming, ad-hoc sensor networks, distributed data redundancy, information replication.
The availability of video imagery through reduction in sensor size, cost, and power has enabled an explosion of collection opportunities. With the increased amount of imagery there is a need to understand the usefulness of video for applications such as Activity-Based Intelligence (ABI), situation understanding, and event-based processing. In this paper, we explore some of the emerging developments in video observations with a focus on cloud technology. Cloud technology supports integration of multiple algorithms, storage of large data sets, indexing over multimedia, and workflow opportunities between humans and machines. We highlight multiple tools such as GeoFlix™, Application Knowledge Interface To Algorithms (AKITA™), and Intelligence Preparation of the Operational Environment (IPOE) using the Ozone Widget Framework (OWF) for permissive surveillance, data to decisions, and information fusion. These tools enable data analytics, algorithm comparison, and user-defined visualizations. An example is presented for target localization and tracking through planned video observations.
The agent-based service composition system considers a service as an intelligent agent capability. In this system, a multi-agent system is considered as a Semantic Web Service composition system in which different agents represent the different individual services. The presented chapter discusses the basics of agent based service composition. An overview of some of the multi-agent based Semantic Web service composition approaches available in the literature has also been given. The chapter also presents models for agent-based Semantic Web service composition basically varying on the use of a coordinator agent in the composition process. Where, the coordinator agent is any agent that can control and coordinate all the different activities involved in the composition process. A brief overview of a service selection model providing formalization of various Quality of Service (QoS) parameters and cognitive parameters of agent for selecting the most appropriate service provider agent has also been presented.
