248 research outputs found
What and where : an empirical investigation of pointing gestures and descriptions in multimodal referring actions
Pointing gestures are pervasive in human
referring actions, and are often combined
with spoken descriptions. Combining gesture
and speech naturally to refer to objects
is an essential task in multimodal NLG
systems. However, the way gesture and
speech should be combined in a referring
act remains an open question. In particular,
it is not clear whether, in planning a
pointing gesture in conjunction with a description,
an NLG system should seek to
minimise the redundancy between them,
e.g. by letting the pointing gesture indicate
locative information, with other, nonlocative
properties of a referent included
in the description. This question has a
bearing on whether the gestural and spoken
parts of referring acts are planned separately
or arise from a common underlying
computational mechanism. This paper
investigates this question empirically, using
machine-learning techniques on a new
corpus of dialogues involving multimodal
references to objects. Our results indicate
that human pointing strategies interact
with descriptive strategies. In particular,
pointing gestures are strongly associated
with the use of locative features in
referring expressions.peer-reviewe
Design issues for agent-based resource locator systems
While knowledge is viewed by many as an asset, it is often difficult to locate particularitems within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together ith the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process
A Review on Intelligent Agent Systems
Multi-agent system (MAS) is a common way of exploiting the potential power of agent by combining many agents in one system. Each agent in a multivalent system has incomplete information and is in capable of solving entire problem on its own. Multi-agent system offers modularity. If a problem domain is particularly complex, large and contain uncertainty, then the one way to address, it to develop a number of functional specific and modular agent that are specialized at solving various problems individually. It also consists of heterogeneous agents implemented by different tool and techniques. MAS can be defining as loosely coupled network of problem solvers that interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver. These problem solvers, often ailed agent are autonomous and can be heterogeneous in nature. MAS is followed by characteristics, Future application, What to be change, problem solving agent, tools and techniques used, various architecture, multi agent applications and finally future Direction and conclusion. Various Characteristics are limited viewpoint, effectively, decentralized; computation is asynchronous, use of genetic algorithms. It has some drawbacks which must be change to make MAS more effective. In the session of problem solving of MAS, the agent performance measure contains many factors to improve it like formulation of problems, task allocation, organizations. In planning of multivalent this paper cover self-interested multivalent interactions, modeling of other agents, managing communication, effective allocation of limited resources to multiple agents with managing resources. Using of tool, to make the agent more efficient in task that are often used. The architecture o MAS followed by three layers, explore, wander, avoid obstacles respectively. Further different and task decomposition can yield various architecture like BDI (Belief Desire Intension), RETSINA. Various applications of multi agent system exist today, to solve the real-life problems, new systems are being developed two distinct categories and also many others like process control, telecommunication, air traffic control, transportation systems, commercial management, electronic commerce, entertainment applications, medical applications. The future aspect of MAS to solve problems that are too large, to allow interconnection and interoperation of multiple existing legacy systems etc
Ontology Domain Modeling Support for Multilingual Servicies in e-commerce: MKBEEM
One of the main objectives of a truly user-friendly
Information Society is to focus on advanced human language
technologies enabling cost-effective interchange across language and culture and more natural interfaces to digital services. The recently launched IST-1999-10589 project MKBEEM (Multilingual Knowledge Based European Electronic Marketplace, 1st Feb. 2000 - 1st Aug. 2002) is rightly in that direction and the work will address basically, written language technologies and its use in the key sector of global business and electronic commerce. In particular MKBEEM will focus on adding multilinguality to all stages of the information cycle, including multilingual content generation and maintenance, automated translation and interpretation and enhancing the natural interactivity and usability of the service with unconstrained language input. On the Knowledge engineering side, the MKBEEM Ontologies will provide a consensual representation of the electronic commerce field in three typical Domains (Tourism, Mail order, Retailers) allowing the exchanges independently of the language of the end user, the service, or the content provider. Ontologies will be used for classifying and indexing catalogues, for filtering user’s query, for facilitating multilingual man-machine dialogues between user and software agent, and for inferring information that is relevant to the user’s request. This paper concentrates on ontology issues, while the used human language processing approaches will be presented closely in our later papers
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A survey on online monitoring approaches of computer-based systems
This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use
Applications of agent architectures to decision support in distributed simulation and training systems
This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool.
The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees.
The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
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