Evaluation of Adaptive Systems

2005

Empirical studies with adaptive systems offer many advantages and opportunities. Nevertheless, there is still a lack of evaluation studies. This paper lists several problems and pitfalls that arise when evaluating an adaptive system and provides guidelines and recommendations for workarounds or even avoidance of these problems. Among other things the following issues are covered: relating evaluation studies to the development cycle; saving resources; specifying control conditions, sample and criteria; asking users for adaptivity effects; reporting results. An overview of existing evaluation frameworks shows which of these problems have been addressed in which way.

2011

Empirical studies with adaptive systems offer many advantages and opportunities. Nevertheless, there is still a lack of evaluation studies. This paper lists several problems and pitfalls that arise when evaluating an adaptive system and provides guidelines and recommendations for workarounds or even avoidance of these problems. Among other things the following issues are covered: relating evaluation studies to the development cycle; saving resources; specifying control conditions, sample and criteria; asking users for adaptivity effects; reporting results. An overview of existing evaluation frameworks shows which of these problems have been addressed in which way.

Many writers and theoriticians have explored concepts and theories relating to complex adaptive systems. Complex adaptive systems concepts stem from seminal contributions by Jantsch, Prigogine and Stengers, Maturana and Varela, Gell-Mann, and Holland. To date, however, no one has bothered to create a nominal definition to define what a complex adaptive system is. This paper presents a concise definition that serves that purpose.

Procedia Computer Science, 2013

Applications of Mathematics in Models, Artificial Neural Networks and Arts, 2010

This chapter has the objective of describing the structure and placing in a taxonomy the Artificial Adaptive Systems (AAS). These systems form part of the vast world of Artificial Intelligence (AI) nowadays called more properly Artificial Sciences (AS). Artificial Sciences means those sciences for which an understanding of natural and/or cultural processes is achieved by the recreation of those processes through automatic models. In particular, Natural Computation tries to construct automatic models of complex processes, using the local interaction of elementary micro-processes, simulating the original process functioning. Such models organize themselves in space and time and connect in a non-linear way to the global process they are part of, trying to reproduce the complexity through the dynamic creation of specific and independent local rules that transform themselves in relation to the dynamics of the process. Natural Computation constitutes the alternative to Classical Computation (CC). This one, in fact, has great difficulty in facing natural/cultural processes, especially when it tries to impose external rules to understand and reproduce them, trying to formalize these processes in an artificial model. In Natural Computation ambit, Artificial Adaptive Systems are theories which generative algebras are able to create artificial models simulating natural phenomenon. The learning and growing process of the models is isomorphic to the natural process evolution, that is, it's itself an artificial model comparable with the origin of the natural process. We are dealing with theories adopting the "time of development" of the model as a formal model of "time of process" itself. Artificial Adaptive Systems comprise Evolutive Systems and Learning Systems. Artificial Neural Networks are the more diffused and best-known Learning Systems models in Natural Computation.

Towards Hybrid and Adaptive Computing, 2010

2001

The development of an online database for studies of empirical evaluations of adaptive systems (EASy-D) is proposed. Such a database will serve as reference for researchers in the field of adaptive systems and as guide for planning new evaluations. It aims at encouraging more evaluations that fulfill certain methodological requirements. The structure of the database records is discussed and the functionality of the web interface is described.

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008

This chapter has the objective of describing the structure and placing in a taxonomy the Artificial Adaptive Systems (AAS). These systems form part of the vast world of Artificial Intelligence (AI) nowadays called more properly Artificial Sciences (AS). Artificial Sciences means those sciences for which an understanding of natural and/or cultural processes is achieved by the recreation of those processes through automatic models. In particular, Natural Computation tries to construct automatic models of complex processes, using the local interaction of elementary micro-processes, simulating the original process functioning. Such models organize themselves in space and time and connect in a non-linear way to the global process they are part of, trying to reproduce the complexity through the dynamic creation of specific and independent local rules that transform themselves in relation to the dynamics of the process. Natural Computation constitutes the alternative to Classical Computation (CC). This one, in fact, has great difficulty in facing natural/cultural processes, especially when it tries to impose external rules to understand and reproduce them, trying to formalize these processes in an artificial model. In Natural Computation ambit, Artificial Adaptive Systems are theories which generative algebras are able to create artificial models simulating natural phenomenon. The learning and growing process of the models is isomorphic to the natural process evolution, that is, it's itself an artificial model comparable with the origin of the natural process. We are dealing with theories adopting the "time of development" of the model as a formal model of "time of process" itself. Artificial Adaptive Systems comprise Evolutive Systems and Learning Systems. Artificial Neural Networks are the more diffused and best-known Learning Systems models in Natural Computation.

Daedalus, 1992

International Journal of General Systems, 2009

In the 1950s and 1960s Ross Ashby created a general theory of adaptive systems. His work is well-known among cyberneticians and systems scientists, but not in other fields. This is somewhat surprising, because his theories are more general versions of the theories in many fields. The philosophy of science claims that more general theories are preferred because a small number of propositions can explain many phenomena. Why, then, are Ashby's theories not widely known and praised? Do scientists really strive for more general, parsimonious theories? This paper reviews the content of Ashby's theories, discusses what they reveal about how scientists work, and suggests what their role might be in the academic community in the future.