Optimizing drug dosages is essential for effective treatment. Clinical protocols may not suit all types of patients evenly, due to many drug trials not being designed to account for all comorbities or clinically relevant outcomes. Methodologies to optimize drug policies with observational data exist, but struggle due to limited data completeness in clinical settings. Computational methods can help overcome these challenges by leveraging field knowledge. This paper proposes an Individualized Doser (IDoser), a core dosing model that links drug dose to relevant covariates via a set of coefficients and includes a loss function to code needed assumptions and requirements. Coordinate descent is used to obtain a fitted model with minimal loss. The loss function also measures performance when validating the model with unseen data. We validated the proposed approach using the case of follicle-stimulating hormone (FSH) dosing for controlled ovarian stimulation (COS). When compared to clinical practice, IDoser achieved a net improvement of up to 31.97% in the validation cases. We present a simple but effective method to bridge the gap between current clinical dosing policies and gold policies based on the true underlying and often unknown dose–response functions.

This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. Anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence.

In this article we tackle the problem of maximizing cooperation among self-interested agents in a resource exchange environment. Our main concern is the design of mechanisms for maximizing cooperation among self-interested agents in a way that their profits increase by exchanging or trading with resources. Although dynamic coalition formation and partner switching (rewiring) have been shown to promote the emergence and maintenance of cooperation for self-interested agents, no prior work in the literature has investigated whether merging both mechanisms exhibits positive synergies that lead to increase cooperation even further. Therefore, we introduce and analyze a novel dynamic coalition formation mechanism, that uses partner switching, to help self-interested agents to increase their profits in a resource exchange environment. Our experiments show the effectiveness of our mechanism at increasing the agents’ profits, as well as the emergence of trading as the preferred behavior over different types of complex networks.

Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.

Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.

This article defines and analyzes a collection of algorithms for persistent storage of data at specific geographical zones exploiting the memory of mobile devices located in these areas. Contrarily to other approaches for data dissemination, our approach uses a viral programming model. Data performs an active role in the storage process. It acts as a virus or a mobile agent, finding its own storage and relocating when necessary. We consider geographical areas of any shape and size. Simulation results show that our algorithms are scalable and converge quickly, even though none of them outperform the others for all performance metrics considered.

The optimisation in dynamic and noisy environments brings closer real-world optimisation. One interesting proposal to adapt the PSO for working in dynamic and noisy environments was the incorporation of an evaporation mechanism. The evaporation mechanism avoids the detection of environment changes, providing a continuous adaptation to the environment changes and reducing the effect when the fitness function is subject to noise. However, its performance decreases when the fitness function is not subjected to noise (with respect to methods that use environment change detection). In this paper we propose a new dynamic evaporation policy to adapt the PSO algorithm to dynamic and noisy environments. Our approach improves the performance when the fitness function is dynamic and not subject to noise. It also keeps a similar performance when the fitness function is subject to noise.

Sensor networks (SN) are rapidly becoming the tool of choice for monitoring. Their versatility makes them useful in numerous and diverse application domains. However, most SN deployments assume that the area and events to monitor/control are well known/understood at design time. Thus, sensors' configurations can be defined prior to their deployment. Nevertheless, when the purpose of an SN is to monitor the events of an uncharted environment, where the distribution and nature of events is uncertain, it is rather intricate to configure its sensors at design time. Instead, sensors should be able to self-configure at run time. In this paper, we propose a low-cost (in terms of energy and computation) collective approach that allows the sensors in an SN to collaboratively search for their most appropriate configurations only using their local knowledge. We empirically show that our approach can help sensors efficiently monitor environments where various dynamic events exist.

The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine regions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phytoplankton structures. Because the system has to work inboard, a main goal of our approach is to dramatically reduce the dimensionality of the problem. Specifically, our development uses two AI techniques, namely Particle Swarm Optimization and Case-Based Reasoning.We report results of experiments performed with simulated environments.

Understanding the way performers use expressive resources of a given instrument to communicate with the audience is a challenging problem in the sound and music computing field. Working directly with commercial recordings is a good opportunity for ta

Dealing with imprecise information is a common characteristic in real-world problems. Specifically, when the source of the information are physical sensors, a level of noise in the evaluation has to be assumed. Particle Swarm Optimization is a technique that presented a good behavior when dealing with noisy fitness functions. Nevertheless, the problem is still open. In this paper we propose the use of the evaporation mechanism for managing with dynamic multi-modal optimization problems that are subject to noisy fitness functions. We will show how the evaporation mechanism does not require the detection of environment changes and how can be used for improving the performance of PSO algorithms working in noisy environments.

We propose a computational model that facilitates agents in a MAS to collaboratively evolve their norms to reach the best norm conventions. Our approach borrows from the social contagion phenomenon to exploit the notion of positive infection: agents with good behaviors become infectious to spread their norms in the agent society. By combining infection and innovation, our computational model helps a MAS establish better norm conventions even when a sub-optimal one has fully settled in the population.

We present a society of personal information agents that work for a community of users and that are aware of the physical and social context of their users. We show how context-awareness is a feature that allows the agents to improve their performance when they work with limited resources in information spaces with a large amount of information. The use of context information allows the agents to focus their information search and, as a result of this, increase the quantity and quality of information delivered to the user. Moreover, we propose an implemented agent architecture with context-aware capabilities. We discuss this architecture in detail, focusing on the capability of exploiting the windows of opportunity provided by the awareness of the users' activity. The agents use these windows of opportunity to furnish the user with information and advice in the situation where it can be most useful. In particular, we show how context-aware information agents can assist a community of attendees to big conferences and fairs. In this application, an information agent gathers relevant information based on a model of specific interests of a user. Given the multiplicity of interesting events, and their distribution in time and space, an information agent has to deliver the gathered information in a few hours and comply to the schedule constraints. Finally, we report some experimentation results to illustrate how context-awareness improve the service a society of information agents provides to a community of users in the conference application.

This article addresses an extension of the knowledge modeling approaches, namely to multi-agent systems where communication and coordination are necessary. We propose the notion of competent agent and define the basic capabilities of these agents for the extension to be effective. An agent is competent when it is capable of reasoning about its own competence and that of the other agents with which it cooperates in a given domain. In our framework, an agent has competence models of itself and of its acquaintances from which it can decide, for a specific problem to be solved, the type of cooperative activity it can request and from which agent. In this paper we focus on societies of peer agents, i.e. agents that are able to solve the same type of task but that may have different degrees of competence for specific problem ranges.