Negotiation-Based Scheduling of Scientific Grid Workflows Through Advance Reservations

In its broadest sense, scheduling of Grid applications can be viewed as a negotiation process between a scheduling service optimising user-centric objectives such as execution time, and a resource manager optimising provider-centric metrics such as resource utilisation or fairness. In this paper we enhance an existing list scheduling algorithm designed for minimising the workflow makespan with advance reservation-based negotiation functionality. As an instantiation of the new negotiation phase, we investigate two advance reservation functionality from the resource provider perspective: attentive and progressive. We illustrate through real-world experiments a two-fold benefit of our approach: improved execution predictability from the user’s perspective, and higher resource utilisation fairness through a new progressive allocation strategy from the provider’s perspective.     

Knowledge-empowered automated negotiation system for e-Commerce

This paper focuses on knowledge empowered automated negotiation systems for buyer-centric multi-bilateral multi-attribute e-Procurement. We propose two knowledge empowered models, namely KERM and KACM. KERM is used for the buyer to determine a list of suppliers which are the best qualified candidates to negotiate with. The use of knowledge features largely in the model, which incorporates both the buyer’s and supplier’s profiles in evaluating a quote. Historical trade records of a supplier contribute to the supplier’s profile credit and therefore the rank of the supplier’s quote. KERM also allows the flexibility to assign appropriate weights, based on buyer’s interests, to each knowledge factor affecting the overall evaluation result of a quote. The resulted list of quotes of high rank is believed to produce satisfactory negotiation result for the buyer. KACM enables an automated concession process, while at the same time facilitates a flexible negotiation via the use of concept switch and tagged rules. Different from other negotiation models, KACM emphasizes the utilization of knowledge originated from the historical negotiation data in estimating and fine-tuning the negotiation parameters, for improving the performance of automated negotiation. Graph results show that our software prototype system makes significant improvement in the satisfaction level of negotiation results.

Knowledge Management in Locating the Patient in an Emergency Medical Service in Italy

This study examines an Emergency Medical Service in order to analyze the composite set of activities and instruments directed at locating the patient. The good management of information about the location of the emergency is highly relevant for a reliable rescue service, but this information depends on knowledge of the territory that is socially distributed between EMS operators and callers. Accordingly, the decision-making process often has to go beyond the emergency service protocols, engaging the operator in undertaking an open negotiation in order to transform the caller’s role from layman to “co-worker”. The patient’s location turns out to be an emerging phenomenon, collaborative work based on knowledge management involving two communities—the callers and the EMS operators—that overlap partially. Drawing examples from emergency calls, the study analyzes the practice of locating a patient as a complex and multi-layered process, highlighting the role played by new and old technologies (the information system and the paper maps) in this activity. We argue that CSCW technologies enable the blended use of different kinds of instruments and support an original interconnection between the professional localization systems and the public’s way of defining a position.     

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.     

Organisational adaptation of multi-agent systems in a peer-to-peer scenario

Organisations in multi-agent systems (MAS) have proven to be successful in regulating agent societies. Nevertheless, changes in agents’ behaviour or in the dynamics of the environment may lead to a poor fulfilment of the system’s purposes, and so the entire organisation needs to be adapted. In this paper we focus on endowing the organisation with adaptation capabilities, instead of expecting agents to be capable of adapting the organisation by themselves. We regard this organisational adaptation as an assisting service provided by what we call the Assistance Layer. Our generic Two Level Assisted MAS Architecture (2-LAMA) incorporates such a layer. We empirically evaluate this approach by means of an agent-based simulator we have developed for the P2P sharing network domain. This simulator implements 2-LAMA architecture and supports the comparison between different adaptation methods, as well as, with the standard BitTorrent protocol. In particular, we present two alternatives to perform norm adaptation and one method to adapt agents’ relationships. The results show improved performance and demonstrate that the cost of introducing an additional layer in charge of the system’s adaptation is lower than its benefits.     

Exploring agent support at the user interface in e-commerce applications

In this paper, we present some of the results from our ongoing research work in the area of ‘agent support’ for electronic commerce, particularly at the user interface level. Our goal is to provide intelligent agents to assist both the consumers and the vendors in an electronic shopping environment. Users with a wide variety of different needs are expected to use the electronic shopping application and their expectations about the interface could vary a lot. Traditional studies of user interface technology have shown the existence of a ‘gap’ between what the user interface actually lets the users do and the users’ expectations. Agent technology, in the form of personalized user interface agents, can help to narrow this gap. Such agents can be used to give a personalized service to the user by knowing the user’s preferences. By doing so, they can assist in the various stages of the users’ shopping process, provide tailored product recommendations by filtering information on behalf of their users and reduce the information overload. From a vendor’s perspective, a software sales agent could be used for price negotiation with the consumer. Such agents would give the flexibility offered by negotiation without the burden of having to provide human presence to an online store to handle such negotiations. Published online: 25 July 2001     

Formation conditions of mutual adaptation in human-agent collaborative interaction

When an adaptive agent works with a human user in a collaborative task, in order to enable flexible instructions to be issued by ordinary people, it is believed that a mutual adaptation phenomenon can enable the agent to handle flexible mapping relations between the human user’s instructions and the agent’s actions. To elucidate the conditions required to induce the mutual adaptation phenomenon, we designed an appropriate experimental environment called “WAITER” (Waiter Agent Interactive Training Experimental Restaurant) and conducted two experiments in this environment. The experimental results suggest that the proposed conditions can induce the mutual adaptation phenomenon.     

User—robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy

This paper describes a hands-off socially assistive therapist robot designed to monitor, assist, encourage, and socially interact with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot’s personality in the hands-off therapy process, focusing on the relationship between the level of extroversion–introversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward customized post-stroke rehabilitation therapy based on the user’s personality traits and task performance. Three validation experiment sets are described. The first maps the user’s extroversion–introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing. The second and the third experiments adjust the personality matching dynamically to adapt the robot’s therapy styles based on user personality and performance. The reported results provide first evidence for user preference for personality matching in the assistive domain and demonstrate how the socially assistive robot’s autonomous behavior adaptation to the user’s personality can result in improved human task performance. This work was supported by USC Women in Science and Engineering (WiSE) Program and the Okawa Foundation.     

Timely Timelessness: Traditional Proportions and Modern Practice in Kahn’s Kimbell Museum

The twentieth century witnessed declining interest in architectural proportioning systems, which were virtually eclipsed by technical, social and fiscal agendas. Louis Kahn is a seminal architect, whose most acclaimed building, the Kimbell Art Museum (1966–72), represents a compelling case-study in the use proportions by twentieth-century architects. In spite of a raft of peculiarly modern restrictions (both technological and programmatic), Kahn appears – despite his espoused ambivalence concerning proportion – to have intentionally produced a building with an array of approximate geometrical as well as precise harmonic proportions. This two-part paper presents the findings of a multifaceted research project that examined the Kimbell’s proportions from numerous standpoints. Part 1 presents a textural analysis of Kahn’s statements regarding proportion, as well as the findings of an archival study of correspondence between the architect and his client and consultants. Part 2 presents a prima facie geometrical analysis of the construction drawings for the project. The division into parts reflects an apparent discrepancy between Kahn’s buildings and what he had to say about them.     

Adaptation of robot’s perception of fuzzy linguistic information by evaluating vocal cues for controlling a robot manipulator

This article proposes a method for adapting a robot’s perception of fuzzy linguistic information by evaluating vocal cues. The robot’s perception of fuzzy linguistic information such as “very little” depends on the environmental arrangements and the user’s expectations. Therefore, the robot’s perception of the corresponding environment is modified by acquiring the user’s perception through vocal cues. Fuzzy linguistic information related to primitive movements is evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues for modifying the BEN. The user’s satisfactory level for the robot’s movements and the user’s willingness to change the robot’s perception are identified based on a series of vocal cues to improve the adaptation process. A situation of cooperative rearrangement of the user’s working space is used to illustrate the proposed system by a PA-10 robot manipulator.     

Agent negotiation based ontology refinement process and mechanisms for service applications

Nowadays organisations are willing to outsource their business processes as services and make them accessible via the Web. In doing so, they can dynamically combine individual services to their service applications. However, unless the data on the Web can be meaningfully shared and is interpretable, this objective cannot be realised. In this paper, a new agent-based approach for managing ontology evolution in a Web services environment is exploited. The proposed approach has several key characteristics such as flexibility and extensibility that differentiate this research from others. The refinement mechanisms which cope with an evolving ontology are carefully examined. The novelty of our work is that inter-processes between different ontologies are studied from the agent’s perspective. Based on this perspective, an agent negotiation model is applied to reach an agreement regarding ontology discrepancy in an application. The efficiency and effectiveness of reaching an agreement over an ontology dispute is leveraged by the private negotiation strategy applied in the argumentation approach. An extended negotiation strategy is discussed to enable sufficient information in decision making at each negotiation round. A case study is presented to demonstrate ontology refinement in a Web services environment.     

An agent architecture for multi-attribute negotiation using incomplete preference information

A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a “guessing” heuristic, by which an agent uses the history of the opponent’s bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.     

Deadline-constrained media uploading systems

This paper presents the design, implementation, and performance evaluation of a novel file uploading system. The system automatically uploads multimedia files to a centralized server given a client machine’s hard deadline—the time when a client machine will exhaust its available storage space due to on-going recording of media files. If existing files have not been uploaded and removed from the client machine’s hard disk by the deadlines, existing files may be overwritten or new files may not get created. Our uploading system was designed to provide a practical solution for emerging business needs. For instance, our system can be used in medical practice to gather videos generated from medical devices located in various procedure rooms for post-procedure analysis, and in law enforcement to collect video recordings from police cars during routine patrolling. Here we investigate two upload scheduling algorithms that determine which client machine should upload its file(s) first. We introduce two emergency control algorithms to handle situations when a client machine is about to exhaust its hard disk space. We evaluate the proposed algorithms via simulations and analysis. Our performance studies show that the upload scheduling algorithms and the emergency control algorithms have a significant impact on overall system performance.

Incremental View Maintenance for Mobile Databases

In a mobile environment, querying a database at a stationary server from a mobile client is expensive due to the limited bandwidth of a wireless channel and the instability of the wireless network. We address this problem by maintaining a materialized view in a mobile client's local storage. Such a materialized view can be considered as a data warehouse. The materialized view contains results of common queries in which the mobile client is interested. In this paper, we address the view update problem for maintaining the consistency between a materialized view at a mobile client and the database server. The content of a materialized view could become incoherent with that at the database server when the content of the database server and/or when the location of the client is changed. Existing view update mechanisms are ‘push-based’. The server is responsible for notifying all clients whose views might be affected by the changes in database or the mobility of the client. This is not appropriate in a mobile environment due to the frequent wireless channel disconnection. Furthermore, it is not easy for a server to keep track of client movements to update individual client location-dependent views. We propose a ‘pull-based’ approach that allows a materialized view to be updated at a client in an incremental manner, requiring a client to request changes to its view from the server. We demonstrate the feasibility of our approach with experimental results. Received 27 January 1999 / Revised 26 November 1999 / Accepted 17 April 2000     

How do Experts Adapt their Explanations to a Layperson’s Knowledge in Asynchronous Communication? An Experimental Study

Despite a plethora of recommendations for personalization techniques, such approaches often lack empirical justification and their benefits to users remain obscure. The study described in this paper takes a step towards filling this gap by introducing an evidence-based approach for deriving adaptive interaction techniques. In a dialogue experiment with 36 dyads of computer experts and laypersons, we observed how experts tailored their written explanations to laypersons’ communicational needs. To support adaptation, the experts in the experimental condition were provided with information about the layperson’s knowledge level. In the control condition, the experts had no available information. During the composition of their answers, the experts in both conditions articulated their planning activities. Compared with the control condition, the experts in the experimental condition made a greater attempt to form a mental model about the layperson’s knowledge. As a result, they varied the type and proportion of the information they provided depending on the layperson’s individual knowledge level. Accordingly, such adaptive explanations helped laypersons reduce comprehension breakdowns and acquire new knowledge. These results provide evidence for theoretical assumptions regarding cognitive processes in text production and conversation. They empirically ground and advance techniques for adaptation of content in adaptive hypermedia systems. They are suggestive of ways in which explanations in recommender and decision support systems could be effectively adapted to the user’s knowledge background and goals.     

User-adaptive explanatory program visualization: evaluation and insights from eye movements

User-adaptive visualization and explanatory visualization have been suggested to increase educational effectiveness of program visualization. This paper presents an attempt to assess the value of these two approaches. The results of a controlled experiment indicate that explanatory visualization allows students to substantially increase the understanding of a new programming topic. Furthermore, an educational application that features explanatory visualization and employs a user model to track users’ progress allows students to interact with a larger amount of material than an application which does not follow users’ activity. However, no support for the difference in short-term knowledge gain between the two applications is found. Nevertheless, students admit that they prefer the version that estimates and visualizes their progress and adapts the learning content to their level of understanding. They also use the application’s estimation to pace their work. The differences in eye movement patterns between the applications employing adaptive and non-adaptive explanatory visualizations are investigated as well. Gaze-based measures show that adaptive visualization captivates attention more than its non-personalized counterpart and is more interesting to students. Natural language explanations also accumulate a big portion of students’ attention. Furthermore, the results indicate that working memory span can mediate the perception of adaptation. It is possible that user-adaptation in an educational context provides a different service to people with different mental processing capabilities.     

The Cortexionist architecture: behavioural intelligence of artificial creatures

Traditionally, producing intelligent behaviours for artificial creatures involves modelling their cognitive abilities. This approach raises two problems. On the one hand, defining manually the agent’s knowledge is a heavy and error-prone task that implies the intervention of the animator. On the other hand, the relationship between cognition and intelligence has not been theoretically nor experimentally proven so far. The ecological approaches provide a solution for these problems, by exploring the links between the creature, its body and its environment. Using an artificial life approach, we propose an original model of memory based on the synthesis of several neuroscience theories. The Cortexionist controller integrates cortex-like structure into a connectionist architecture in order to enhance the agent’s adaptation in a dynamic environment, ultimately leading to the emergence of intelligent behaviour. Initial experiments presented in this paper prove the validity of the model.     

Rapid behavior adaptation for human-centered robots in a dynamic environment based on the integration of primitive confidences on multi-sensor elements

This article presents a method for tele-operated mobile robots to rapidly adapt to behavior policies. Since real-time adaptation requires frequent observations of sensors and the behavior of users, rapid policy adaptation cannot be achieved when significant data are not differentiated from insignificant data in every process cycle. Our method solves this problem by evaluating the significance of data for learning based on changes in the degree of confidence. A small change in the degree of confidence can be regarded as reflecting insignificant data for learning (that data can be discarded). Accordingly, the system can avoid having to store experience data too frequently, and the robot can adapt more rapidly to changes in the user’s policy. In this article, we confirm that by taking advantage of a significance evaluation not only of proposition of behavior, but also of each proposition of each piece of sensor-level data, a robot can rapidly adapt to a user’s policy. We discuss the results of two experiments in static and dynamic environments, in both of which the user switched policies between “avoid” and “approach.”     

Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system

Due to rapid changes of markets and pressures of competitions, industries are adopting their production ways to support diversity of customer’s needs and increase of new product developments. This paper deals with development of an agent-based architecture of dynamic systems for process planning in the manufacturing systems. In consideration of alternative manufacturing processes and machine tools, the process plans and the schedules of the manufacturing resources are generated incrementally and dynamically. A previously proposed negotiation protocol is customized and improved to generate suitable process plans for the target products real-timely and dynamically, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks and the suitable process plans are searched and generated to cope with both the dynamic status and disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans.