Attempts to develop a drug treatment for female sexual interest/arousal disorder have so far been guided by the principle of ‘one size fits all’, and have failed to acknowledge the complexity of female sexuality. Guided by personalized medicine, we designed two on-demand drugs targeting two distinct hypothesized causal mechanisms for this sexual disorder. The objective of this study was to design and test a novel procedure, based on genotyping, that predicts which of the two on-demand drugs will yield a positive treatment response. In a double-blind, randomized, placebo-controlled cross-over experiment, 139 women with female sexual interest/arousal disorder received three different on-demand drug-combination treatments during three 2-week periods: testosterone 0.5 mg + sildenafil 50 mg, testosterone 0.5 mg + buspirone 10 mg, and matching placebo. The primary endpoint was change in satisfactory sexual events. Subjects’ genetic profile was assessed using a microarray chip that measures 300,000 single-nucleotide polymorphisms. A preselection of single-nucleotide polymorphisms associated with genes that are shown to be involved in sexual behaviour were combined into a Phenotype Prediction Score. The Phenotype Prediction Score demarcation formula was developed and subsequently validated on separate data sets. Prediction of drug-responders with the Phenotype Prediction Score demarcation formula gave large effect sizes (d = 0.66 through 1.06) in the true drug-responders, and medium effect sizes (d = 0.51 and d = 0.47) in all patients (including identified double, and non-responders). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the Phenotype Prediction Score demarcation formula were all between 0.78 and 0.79, and thus sufficient. The resulting Phenotype Prediction Score was validated and shown to effectively and reliably predict which women would benefit from which on-demand drug, and could therefore also be useful in clinical practice, as a companion diagnostic establishing the way to a true personalized medicine approach.
Abstract. We present a multiagent decision mechanism for the airport traffic control domain. It enables airlines to jointly decide on proposals for plan conflict solutions. The mechanism uses weighted voting for maximizing global utility and Clarke Tax to discourage manipulation. We introduce accounts to ensure that all agents are treated fairly, to some extent. The mechanism allows an airport to determine the pay-off between optimality and fairness of schedules. Also, it compensates for agents that happen to be in practically unfavourable positions.
In this paper, we give an operational and denotational semantics for a 3APL metalanguage , with which various 3APL interpreters can be programmed. We moreover prove equivalence of these two semantics. Furthermore, we relate this 3APL metalanguage to object-level 3APL by providing a specific interpreter, the semantics of which will prove to be equivalent to object-level 3APL. 4 In the literature, also the concepts of desires and intentions are often used, besides or instead of goals and plans, respectively. This is however not important for the current discussion. 5 3APL is to be pronounced as "triple-a-p-l". 6 In the original version this was a set of plans. 7 The addition of goals was a recent extension ([18]). 8 A change in the environment is a possible "side effect" of the execution of a basic action.
), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) ForewordIn the last decade, multi-agent systems have both become widely applied and also increasingly complex. The applications include the use of agents as automous decision makers in often safety-critical, dangerous, or high impact scenarios (traffic control, autonomous satellites, computational markets). The complexity arises from the fact that not only do we expect the agent to make decisions in situations that are not anticipated at forehand, the agent also interacts with other complex agents, with humans, organisations, and it lives in a dynamic environment (activators of an agent can fail, communication is prone to error, human response may be ambiguous, rules of an organisation may leave behaviour open or over-constrained, and environments may change 'spontaneously' or as a result of other agents acting upon it).Taking these two facts together call for a rigorous Specification and Verification of Multi-Agent Systems. Since intelligent agents are computational systems, it is no wonder that this activity builds upon and extends concepts and ideas of specifying and verifying computer-based systems in general. For one, an axiom is that the tools are mainly logical, or in any case formal. But although traditional techniques of specification and verification go a long way when reasoning about the correctness of a single agent, there are additional questions already to be asked at this level: do we 'only' require the agent to behave well under a pre-defined set of inputs, or do we want to allow for (partially) undefined scenarios? And do we care about the 'correctness' of an agent's beliefs, desires and intentions during a computation? How do we want to guarantee that the agent 'knows what he is doing', has 'reasonable desires' and 'only drops an intention if it(s goal) is fulfilled, or cannot be reasonably be fulfilled any longer'? And a predecessor of this 'how to guarantee' question is equally important: what do we exactly mean by those requirements?In a multi-agent system, specification and verification becomes only harder and, indeed, more interesting. Once we have an understanding of how to make the agents behave correctly individually, is this property then also compositional, in the sense that it applies to the system as a whole? What are the requirements we need to impose on the interaction among the agents, the ability of the human users, the organisation the agents represent, or the environment as ...
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