Multi-Agent Systems (MAS) design methodologies and Integrated DevelopmentEnvironments exhibit many interesting properties that also support simulation design. Yet, in their current form, they are not appropriate enough to model Multi-Agent Based Simulations (MABS). Indeed, their design is focused on the functionalities to be achieved by the MAS and the allocation of these functionalities among software agents. In that context, the most important point of design is the organization of the agents and how they communicate with each other. On the opposite, MABS aim at studying emergent phenomena, the origin of which lies in the interactions between entities and their interaction with the environment. In that context, the interactions are not limited to exchanging messages but can also be fundamental physical interactions or any other actions involving simultaneously the environment and one or several agents. To deal with this issue, this paper presents the core notions of the Interaction-Oriented Design of Agent simulations (IODA) approach to simulation design. It includes a design methodology, a model, an architecture and also JEDI, a simple implementation of IODA concepts for reactive agents. First of all, our approach focuses on the design of an agent-independent specification of behaviors, called interactions. These interactions are not limited to the analysis phase of simulation: they are made concrete both in the model and at the implementation stage. In addition, no distinction is made between agents and objects: all entities of the simulation are agents. Owing to this principle, designing which interactions occur between agents, as well as how agents act, is achieved by means of an intuitive plugand-play process, where interaction abilities are distributed among the agents. Besides, the guidelines provided by IODA are not limited to the specification of the model as they help the designer from the very beginning towards a concrete implementation of the simulation.
Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.
Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from within-host to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup.renater.fr/www/emulsion-public.
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