Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.

Currently social networks are used by buyers to become more aware of sellers and their goods. Increasingly, they are used with three other market mechanisms: Buyers use social networks to form buyer groups that leverage purchasing power. Sellers incentivize buyers to post recommendations on social media. Sellers or buyers compose compatible items into bundles with increased value and reduced price. In this paper we study the potential effects of social networks on these mechanisms. For comprehensive and consistent coverage, we simulate combinations of these mechanisms, using published metrics for authenticity. Our results show seller profitability increases with the use of social networks applied to all possible combinations.

Service-oriented architectures can be used to provide multiple simultaneous sessions to users that wish to communicate over a variety of media. This gives rise to rich, highly effective communication sessions that can greatly enhance userspsila interaction. For example, a health services virtual organization seeks to use such tools for a variety of purposes: virtual patient simulation, anatomical visualization and virtually sharing cadaveric dissections. We propose SAVOIR, Service-oriented Architecture for Virtual Or- ganization Resource and Infrastructure for this task, where tools and applications are resources and they can be accessed and controlled via Web Services. The purpose of this paper is to present a method for modeling sessions in SAVOIR by using Web Ontology Language (OWL). We express in OWL 1.1 constraints on when sessions can and cannot be run or cannot be run concurrently with the ses- sions now running. There are several types of violations: aggregate bandwidth may exceed capacity, network infras- tructure may not be available, too many users may want to access a limited shared resource, etc. The session scheduler depends on the OWL 1.1 description logic reasoner to evaluate the session for violation of these constraints before the session is allowed to be scheduled.

We consider negotiation protocols in which each offer contains a price and a description from some given ontology. If the opposing negotiation agents do not share the same version of this ontology, for instance because not all have been made aware of the latest changes, then a fixed communication protocol may be expected to fail when one opponent is faced with an offer including a concept novel to it. However, the communication may proceed if the agent is allowed to ask for, receive and assimilate the missing information. This information may come from the other partner, a trusted source, or the human that the agent is serving. We propose a method whereby assimilation is accomplished dynamically so that existing conversations do not need to be abandoned. Our setting employs negotiation protocols that are required to be convergent, i.e. to make progress by exploring a finite negotiation space and thus terminate, either with a mutually acceptable offer or with an indication that no such offer exists. We show that existing convergent negotiation protocols, when applied in a setting that allows assimilation of monotonic additions, retain the property of convergence despite the permissibility of messages that do not meet the condition of making progress. We motivate the work within an e-marketplace where negotiation is over product features and can lead the conversation deeper into specific features, about which some fact may not be mutually known until more information is shared.

Most industries and organizations use collections of tools, devices, and applications that are growing in complexity. New tools or applications may be acquired and old tools may become obsolete over time. They are often running on a variety of platforms, have different bandwidth and QoS requirements, and in most cases they cannot be accessed through a single point of entry. Moreover, some tools may require specific configurations done by technical experts. To address these issues, we propose an extensible, reliable, and simple software architecture that can hide the complexity of provisioning the network and running the tools. This paper introduces a service-oriented approach for creating an agile infrastructure to provision devices, applications, and their underlying networks. The Eucalyptus prototype is developed as an empirical application to test this approach. Eucalyptus is built on a set of generic fine-grained Web services to manage and configure available resources, where new resources can be custom-built or imported from a third party. They can be integrated into Eucalyptus using a set of Web service-enabled APIs. Our user community consists of architects and industrial designers. Eucalyptus can manage and configure the resources needed by geographically distributed groups of architects who need to collaborate in real time on the design of buildings, in a virtual participatory design studio (PDS). Eucalyptus provides a single point of entry for the architects to access a wide variety of tools: videoconference applications, visualization services, rendering services employing parallel computers, etc. Eucalyptus provides a set of upper layer services for users to provision devices and applications running on high-speed broadband networks, as well as the commercial IP networks.

While most service-oriented solutions are developed for system integration or data integration, we propose a Web services-based solution, Eucalyptus, for provisioning applications and networks on demand. Eucalyptus is built on the power of the user-controlled lightpath provisioning (UCLP) tool, which pioneers a user-centric and service-oriented approach for creating and managing a private end-to-end optical network. As a first user of UCLP, Eucalyptus aims to construct an participatory environment for geographically distributed teams of architects and industrial designers with the with the support of configurable broadband switched networks, as well as the traditional routed IP networks. The contribution of Eucalyptus is to provide a proof-of-concept example on how Web service and service-oriented architecture (SoA) can effectively provide on-demand provisioning for heterogenous resources in hybrid networks. These resources can be provisioned, launched, monitored, terminated, and reserved through Web services. Eucalyptus is network and platform neutral. It offers a single point of entry for users to access resources ranging from video conference applications, rendering clusters, to the underlying networks. Each resource is configured through a resource-specific Web service. Eucalyptus also includes a set of generic management Web services to coordinate sessions, to manage resources and users, and to compose workflows, such that the network and the application are properly configured for the users engaging in a participatory design session.

An agent engaged in multi-issue automated negotiation can benefit greatly from learning about its opponent's preferences. Knowledge of the opponent's preferences can help the agent not only to find mutually acceptable agreements more quickly, but also to negotiate deals that are better for the agent in question. In this paper, we describe a new technique for learning about an opponent's preferences by observing its history of offers in a negotiation. Patterns in the similarity between the opponent's offers and our own agent's offers are used to determine the likelihood that the opponent is making a concession at each stage in the negotiation. These probabilities of concession are then used to determine the opponent's most likely preference relation over all offers. Experimental results show that our technique significantly outperforms a previous method that assumes that a negotiation agent will always make concessions during the course of a negotiation.

We present a classification method for learning an opponent's preferences during a bilateral multi-issue negotiation. Similar candidate preference relations are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent's true preference relation over the set of offers lies in that class. Evidence used for classification decision-making is obtained by observing the opponents' sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponent's true preferences.

We consider private information a commodity, of value to both the information holder and the information seeker. Hence, a customer can be enticed to trade his/her private information with a business in exchange for compensation. In this article, we propose to apply utility theory to allow each participant to express the value they place on each private datum and, separately, on combinations of data. The PrivacyPact protocol transmits messages that comprise possible exchanges. Each participant is prevented from making offers that necessarily have lower utility for the other partner than previous ones. The protocol is complete in that if an exchange exists that is acceptable to both, it will be found as long as neither partner exits the negotiation early. While the space of possible offers grows exponentially on the number of negotiable items, experimentation with simple strategies indicates that negotiations can converge relatively quickly.

This paper considers the problem of making decisions in a dynamic environment where one of possibly many bundles of items must be purchased and quotes for items open and close over time. Probability measures on item prices are used when exact prices are not yet known. We show that expected utility estimation can be improved by considering how future information can affect the purchasing agent's behaviour. An efficient Monte Carlo simulation method is presented that determines the expected utility of an option in our decision tree, referred to as a QR-tree, where the number of simulations needed is linear in the size of the tree. In our experiments simulating a purchase agent in a specific market, the expected utility was estimated more than 50 times more accurately than a greedy method that always pursues the bundle with the current highest expected utility.

An individual's survival and comfort depends on the goods he or she possesses and the skills with which he can adapt the environment to his needs. His surrounding community may volunteer to add to his store of possessions or offer services. Beyond that, the individual can meet his needs only through trade with others. To negotiate a trade, the individual and a trading partner must agree upon a real-numbered value assigned to goods and services. Then monetary tokens that represent this value are exchanged for the goods or services. Through electronic commerce, i.e. the use of a computer network to enhance trade, physical distance is no longer a barrier to the search for a trading partner. A widely-held view of the future is that computer networks will increase an individual's chances of finding trading partners with goods or services that meet his need. Besides information about needed and offered goods and services, the network can transmit electronic monetary tokens that cannot be falsely generated, so they truly represent a value in lieu of goods. In some cases the goods and services themselves can be transmitted across the network, such as digital multimedia products, or organizational or reasoning services. Thus electronic commerce is a natural tool for procuring goods and services that improve our survival and comfort.

Traditionally, plan recognition is defined as the process of matching a set of observations from a user to a library of possible plans in a domain, indicating the possible candidate plans of the user. We have developed a system which allows updates to the plan library during plan recognition and which amends the list of candidate plans, without having to restart the plan recognition process. This work builds on H. Kautz's model (1987) of plan recognition and applies truth maintenance. We describe a graphic-oriented tool which we have developed for users of our system. We comment on the insights which we have gained, about the kinds of users and the kinds of modifications which we might want to accommodate. We emphasize the significance of the system, allowing incomplete or incorrect libraries to be amended. We summarize the challenges which remain, both for interface design and plan recognition. Finally, we elaborate on the potential use of our system for a variety of artificial intelligence applications including knowledge based systems, cooperative AI and natural language understanding.>