网范文：“A Comparison of Different Cognitive Paradigms” 本文我将介绍虚拟实验室,实现了五种不同的模型来控制动画，英语毕业论文，一个基于规则的系统。这篇哲学范文讲述了不同达到认知模式。通过不同的实验,我比较的性能模型,得出这样的结论，这项工作的最初目的是为了表明,知识可以由一个认知系统构成，来自自适应行为。我的目的是通过构建人工系统的开发，以这样一种方式,通过它们表现出的知识。在我的目标成功后,发现一个评价问题，自适应行为可以被看作是知识的一种形式,但也亦然。
如何了解系统，并真正获得知识,或如果它只是条件反射,可以从两个角度描述相同的过程吗?我决定尝试澄清这些问题,深入认知科学的基本观念。下面的范文讲述了这一问题。
Abstract
In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no “best” model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical aid, I note that there is no “best” approach for studying cognition, since different approaches have all advantages and disadvantages, because they study different aspects of cognition from different contexts. This has implications for current debates on “proper” approaches for cognition: all approaches are a bit proper, but none will be “proper enough”. I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition.
Introduction
The initial goal of this work was to show that knowledge can be developed by a cognitive system parting from adaptive behaviour. My aim was to do this by building artificial systems which develop in such a way that they exhibit knowledge, but not implemented directly. After a relatively easy success of my goal, I stumbled with an interpretational problem: adaptive behaviour can be seen as a form of knowledge, but also vice versa. So how can we know of the system really acquired knowledge, or if it were just conditioning, when the same process can be described from both perspectives? I decided to try to clarify these issues by going deeper into basic notions of cognitive science. What can we consider being cognition? Cognition comes from the Latin cognoscere, which means ‘get to know’. We can say that cognition consists in the acquisition of knowledge. We can say that a system is cognitive if it knows something. Humans are cognitive systems because they know how to communicate, build houses, etc. Animals are cognitive systems because they know how to survive. Autonomous robots are cognitive systems if they know how to navigate.
Does a tree know when spring comes because it blossoms? We should better slow down, these issues will be discussed in Section 4. In classical cognitive science and artificial intelligence (e.g. Newell and Simon, 1972; Newell, 1990; Shortliffe, 1976; Fodor, 1976; Pylyshyn, 1984; Lenat and Feigenbaum, 1992), people described cognitive systems as symbol systems (Newell, 1980). However, it seemed to become a consensus in the community that if a system did not used symbols or rules, it would not be cognitive. From this perspective, animals are not cognitive systems because they do not use and have symbols. Nevertheless, if we open a human brain, we will not find any symbol either. Opposing the symbolic paradigm, the connectionist approach was developed (Rumelhart, et al., 1986; McClelland, et al., 1986), assuming that cognition emerges from the interaction of many simple processing units or neurons.
To my knowledge, there has been no claim that “therefore a cognitive system should be able to perform parallel distributed processes, otherwise it is not cognitive”. Still, there has been a long discussion on which paradigm is the “proper” one for studying cognition (Smolensky, 1988; Fodor and Pylyshyn, 1988). The behaviour-based paradigm (Brooks, 1986; 1991; Maes, 1994) was developed also opposing the symbolic views, and not entirely different from the connectionist. There have been also other approaches to study cognition (e.g. Maturana and Varela, 1987; Beer, 2017; Gärdenfors, 2017). The actual main goal of this work is to show that there is no single “proper” theory of cognition, but different theories that study cognition from different and with different goals. Moreover, I argue that in theory any cognitive system can be modelled to an arbitrary degree of precision by most of the accepted theories, but none can do this completely (precisely because they are models). I believe that we will have a less-incomplete understanding of cognition if we use all the theories available rather than trying to explain every aspect of cognition from a single perspective.
This view is currently shared by many researchers, but to my knowledge, there has been no empirical study in order to backup these claims. For achieving this, I implemented different models from different paradigms in virtual animats, in order to compare their cognitive abilities. The models I use are not very complex, not to at all to be compared with humans, but they are useful for understanding the generic processes that conform a cognitive system. After doing several comparative experiments, I can suggest, using the simulation results as a philosophical aid, that there is no “best” paradigm, and each has advantages and disadvantages.
In the following section, I present a virtual laboratory developed in order to compare the implementations in animats of models coming from five different perspectives: rule-based systems1 , behaviour-based systems, concept-based systems, neural networks, and Braitenberg architectures. Because of space limitations, I am forced to skip deep introductions to each paradigm for studying cognition, but the interested reader is referred to the proper material. In Section 3, I present experiments in order to compare the performance of the animats in different scenarios. With my results, in Section 4 I discuss that each model is more appropriate for modelling different aspects of cognition, and that there is no “best” model. I also discuss issues about models, and from my results I try to reach a broader notion of cognition merging all the paradigms reviewed. I also propose a simple classification of different types of cognition. In the Appendixes, the reader can find details about the original concept-based model I use and about my virtual laboratory.
Different types of cognition
We can quickly begin to identify different types of cognition, and this will relate the ideas just presented with previous approaches for studying cognition. Of course this does not attempt to be a complete or final categorization, but it should help in understanding my ideas. We can say that classical cognitive science studies human cognition. But of course many disciplines are involved in the study of human cognition, such as neuroscience, psychology, philosophy, etc. Human cognition can be seen as a subset of animal cognition, which has been studied by ethologists (e.g. McFarland, 1981) and behaviour-based roboticists (e.g. Brooks, 1986). But we can also consider the process of life as determined by cognition and vice versa, as the idea of autopoiesis proposes (Maturana and Varela, 1980; 1987; Stewart, 1996), in which we would be speaking about cognition of living organisms.
Here we would run into the debate of what is considered to be alive, but in any case we can say that biology has studied this type of cognition. Artificial cognition would be the one exhibited by systems built by us. These can be built as models of the cognition of the living, such as an expert system, an octapod robot, or my virtual animats. But we can also build artificial systems without inspiration from biology which can be considered as cognitive (the thermostat knows when it is too hot or too cold). Most of these types of cognition can be considered as adaptive cognition, since all living organisms also adapt to modest changes in their environment, but also many artificial and nonliving systems. Cybernetics (Wiener, 1948), and more recently certain branches of artificial intelligence and artificial life (e.g. Holland, 1992) have studied adaptive systems. We can contain all the previous types of cognition under systemic cognition. complex systems (BarYam, 1997), and general systems theory (Turchin, 1977) can be said to have studied this type of cognition. I cannot think of a more general type of cognition because something needs to exhibit this cognition, and that something can always be seen as a system. We can see a graphical representation of these types of cognition in Figure 13.
Conclusions
In classical cognitive science, it seems that there was the common belief that human cognition was a symbol system (Newell, 1990). I believe that the confusion was the following: human cognition can be modelled by symbol systems (at a certain level), but this does not mean that human cognition (absolutely) is a symbol system. But the same applies to all models. Human cognition (absolutely) is not a parallel distributed processor, nor any other model about which we can think. Things do not depend on the models we have of them. That some aspects of cognition (e.g. navigation) are implemented more easily under a certain paradigm, does not mean that natural cognitive systems do it the same way. The implemented animats are cognitive at the same degree, because they model the same aspect of cognition roughly with the same success. Systems are not cognitive because they implement a specific architecture. Of course different architectures can be more parsimonious, others more explanatory, others easier to implement, etc.; but this is dependent of the context in which we are modelling. Different cognitive models and paradigms can be said to be modelling different aspects of cognition. They are different metaphors, with different goals and from different contexts. Therefore, we will have a less-incomplete view of cognition if we take into account as many paradigms as possible.()
网站原创范文除特殊说明外一切图文作品权归所有；未经官方授权谢绝任何用途转载或刊发于媒体。如发生侵犯作品权现象，保留一切法学追诉权。()
更多范文欢迎访问我们主页 当然有需求可以和我们 联系交流。-X()
，英语论文范文 |
