3,417 research outputs found
Evaluating Citebase, an open access Web-based citation-ranked search and impact discovery service
No full textCitebase is a new citation-ranked search and impact discovery service that measures citations of scholarly research papers which are openly accessible on the Web, i.e. papers that are assessable continuously online. Other services, such as ResearchIndex, have emerged in recent years to offer citation indexing of Web research papers. In the first detailed user evaluation of an open access Web citation indexing service, Citebase has been evaluated by nearly 200 users from different backgrounds. The paper details the procedures used in the evaluation, and analyses the results of this study, which took place between June and October 2002. It was found that within the scope of its primary components, the search interface and services available from its rich bibliographic records, Citebase can be used simply and reliably for the purpose intended, and that it compares favourably with other bibliographic services. It is shown tasks can be accomplished efficiently with Citebase regardless of the background of the user. More data need to be collected and the process refined before it is as reliable for measuring citation impact of indexed papers. Better explanations and guidance are required for first-time users. Coverage is seen as a limiting factor, even though Citebase indexes over 200,000 papers from arXiv. Non-physicists were frustrated at the lack of papers from other sciences. The principle of citation searching of open access archives has thus been demonstrated and need not be restricted to current users. Since the evaluation, Citebase has become a featured service of the ArXiv physics eprint archives
What to bid and when to stop
No full textNegotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators.Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent.There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies.To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted.The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios.We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions.Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies.Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance.Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other.Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies.We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model.Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent
Interview with Henry C. Williams
No full textHenry C. Williams, a Tennessee native, served during World War II with the 90th infantry division, 3rd Army. He was inducted in April of 1942, starting as a private and leaving as a staff sergeant in November of 1945. He was present on D-Day at Utah Beach as part of the three-man team working a 30-caliber water-cooled machine gun. He is the author of Combat Boots, a memoir of his time in the service
Accepting Optimally in Automated Negotiation with Incomplete Information (abstract)
No full textIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
A polymorphism of the TIM-1 IgV domain: Implications for the susceptibility to filovirus infection
No full textFiloviruses, including Ebola and Marburg viruses, cause severe hemorrhagic fever in humans and nonhuman primates with mortality rates of up to 90%. Human T-cell immunoglobulin and mucin domain 1 (TIM-I) is one of the host proteins that have been shown to promote filovirus entry into cells. In this study, we cloned TIM-1 genes from three different African green monkey kidney cell lines (Vero E6, COS-I, and BSC-1) and found that TIM-1 of Vero E6 had a 23-amino acid deletion and 6 amino acid substitutions compared with those of COS-1 and BSC-1. Interestingly, Vero E6 TIM-I had a greater ability to promote the infectivity of vesicular stomatitis viruses pseudotyped with filovirus glycoproteins than COS-1-derived TIM-I. We further found that the increased ability of Vero E6 TIM-1 to promote virus infectivity was most likely due to a single amino acid difference between these TIM-1s. These results suggest that a polymorphism of the TIM-I molecules is one of the factors that influence cell susceptibility to filovirus infection, providing a new insight into the molecular basis for the filovirus host range. (C) 2014 Elsevier Inc. All rights reserved
Nostalgia: content, triggers, functions
No full textSeven methodologically diverse studies addressed 3 fundamental questions about nostalgia. Studies 1 and 2 examined the content of nostalgic experiences. Descriptions of nostalgic experiences typically featured the self as a protagonist in interactions with close others (e.g., friends) or in momentous events (e.g., weddings). Also, the descriptions contained more expressions of positive than negative affect and often depicted the redemption of negative life scenes by subsequent triumphs. Studies 3 and 4 examined triggers of nostalgia and revealed that nostalgia occurs in response to negative mood and the discrete affective state of loneliness. Studies 5, 6, and 7 investigated the functional utility of nostalgia and established that nostalgia bolsters social bonds, increases positive self-regard, and generates positive affect. These findings demarcate key landmarks in the hitherto uncharted research domain of nostalgi
A Conversation with Tim O’Brien
No full textTim O’Brien is the author of The Things They Carried. Based on his experiences as a foot soldier during the Vietnam War, the short-story collection published in 1990 helped to define Vietnam War literature and is now widely taught in middle- and high-school curricula. O’Brien’s other books include the novels July, July; In the Lake of the Woods; and Going After Cacciato. His work has earned a National Book Award, the Richard C. Holbrooke Distinguished Achievement Award, and the Pritzker Military Library Literature Award. Now retired from teaching, O’Brien served as the endowed chair of the MFA program at the University of Texas San Marcos biannually from 2003 to 2012
Acceptance conditions in automated negotiation
No full textIn every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance conditions.MediamaticsElectrical Engineering, Mathematics and Computer Scienc
Player agency in interactive narrative: audience, actor & author
No full textThe question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, ‘how can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?’ This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
Role of mutation in pseudomonas aeruginosa biofilm development.
No full textThe survival of bacteria in nature is greatly enhanced by their ability to grow within surface-associated communities called biofilms. Commonly, biofilms generate proliferations of bacterial cells, called microcolonies, which are highly recalcitrant, 3-dimensional foci of bacterial growth. Microcolony growth is initiated by only a subpopulation of bacteria within biofilms, but processes responsible for this differentiation remain poorly understood. Under conditions of crowding and intense competition between bacteria within biofilms, microevolutionary processes such as mutation selection may be important for growth; however their influence on microcolony-based biofilm growth and architecture have not previously been explored. To study mutation in-situ within biofilms, we transformed Pseudomonas aeruginosa cells with a green fluorescent protein gene containing a +1 frameshift mutation. Transformed P. aeruginosa cells were non-fluorescent until a mutation causing reversion to the wildtype sequence occurs. Fluorescence-inducing mutations were observed in microcolony structures, but not in other biofilm cells, or in planktonic cultures of P. aeruginosa cells. Thus microcolonies may represent important foci for mutation and evolution within biofilms. We calculated that microcolony-specific increases in mutation frequency were at least 100-fold compared with planktonically grown cultures. We also observed that mutator phenotypes can enhance microcolony-based growth of P. aeruginosa cells. For P. aeruginosa strains defective in DNA fidelity and error repair, we found that microcolony initiation and growth was enhanced with increased mutation frequency of the organism. We suggest that microcolony-based growth can involve mutation and subsequent selection of mutants better adapted to grow on surfaces within crowded-cell environments. This model for biofilm growth is analogous to mutation selection that occurs during neoplastic progression and tumor development, and may help to explain why structural and genetic heterogeneity are characteristic features of bacterial biofilm populations
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