Anthropic agency: a multiagent system for physiological processes

Multi-agent model of hepatitis C virus infection

OBJECTIVES

The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice.

METHODS AND MATERIALS

In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared.

RESULTS

Important advantages of the proposed method are noted and demonstrated: these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%.

CONCLUSIONS

The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections.

Sequentiality of daily life physiology: an automatized segmentation approach

Based on the hypotheses that (1) a physiological organization exists inside each activity of daily life and (2) the pattern of evolution of physiological variables is characteristic of each activity, pattern changes should be detected on daily life physiological recordings. The present study aims at investigating whether a simple segmentation method can be set up to detect pattern changes on physiological recordings carried out during daily life. Heart and breathing rates and skin temperature have been non-invasively recorded in volunteers following scenarios made of "daily life" steps (13 records). An observer, undergoing the scenario, wrote down annotations during the recording time. Two segmentation procedures have been compared to the annotations, a visual inspection of the signals and an automatic program based on a trends detection algorithm applied to one physiological signal (skin temperature). The annotations resulted in a total number of 213 segments defined on the 13 records, the best visual inspection detected less segments (120) than the automatic program (194). If evaluated in terms of the number of correspondences between the times marks given by annotations and those resulting from both physiologically based segmentations, the automatic program was better than the visual inspection. The mean time lags between annotation and program time marks remain <60 s (the precision of annotation times marks). We conclude that physiological variables time series recorded in common life conditions exhibit different successive patterns that can be detected by a simple trends detection algorithm. Theses sequences are coherent with the corresponding annotated activity.

THE MULTIAGENT TECHNOLOGY AND PARADIGM WITHIN SCIENTIFIC DISCOVERY

Scientific practice has been rapidly evolving in the last years under the pressure of developments in computer science and technology. In this paper we present some of the results of our research activity at the boundary between computer science and philosophy of science started in 1997 under Marco Somalvico's impulse and guidance. In particular, we discuss two roles that multiagent systems can play in scientific discovery. From the one hand, they can support scientific practice; from the other hand, they can represent scientific results. The theoretical framework presented in this paper is exemplified in concrete by illustrating specific implemented systems, both taken from the literature and developed by ourselves.

Combining rate-adaptive cardiac pacing algorithms via multiagent negotiation

Simulating and controlling physiological phenomena are notoriously complex tasks to tackle and require accurate models of the phenomena of interest. Currently, most physiological processes are described by a set of partial models capturing specific aspects of the phenomena, and usually their composition does not produce effective comprehensive models. A current open issue is thus the development of techniques able to effectively describe a phenomenon starting from partial models. This is particularly relevant for heart rate regulation modeling where a large number of heterogeneous partial models exists. In this paper we make the original proposal of adopting a multiagent paradigm, called anthropic agency, to provide a powerful and flexible tool for combining partial models of heart rate regulation for adaptive cardiac pacing applications. The partial models are embedded in autonomous computational entities, called agents, that cooperatively negotiate in order to smooth their conflicts on the values of the variables forming the global model the multiagent system provides. We experimentally evaluate our approach and we analyze its properties.