A comparison study of 12 paradigms for developing embodied agents

摘要

We report on a study in which 12 different paradigms were used to implement agents acting in an environment which borrows elements from artificial life and multi-player strategy games. In choosing the paradigms we strived to maintain a balance between high-level, logic-based approaches and low-level, physics-oriented models; between imperative programming, declarative approaches and 'learning from basics'; between anthropomorphic or biologically inspired models on one hand and pragmatic, performance-oriented approaches on the other. We have found that the choice of the paradigm determines the software development process and requires a different set of skills from the developers. In terms of raw performance, we found that the best performing paradigms were those which (a) allowed the knowledge of human experts to be explicitly transferred to the agent and (b) allowed the integration of well-known, high-performance algorithms. We have found that maintaining a commitment to the chosen paradigm can be difficu there is a strong temptation to offer shallow fixes to perceived performance problems through a 'flight into heuristics'. Our experience is that a development process without the discipline enforced by a central paradigm leads to agents which are a random collection of heuristics whose interactions are not clearly understood. Although far from providing a definitive verdict on the benefits of the different paradigms, our study provided a good insight into what kind of conceptual, technical or organizational problems would a development team face depending on their choice of agent paradigm.