网范文：“A Virtual Laboratory ” 通过开发一个虚拟的实验室测试，英语论文网站，来表现动画控制机制，提出了不同的虚拟环境，可以创建不同的现象,如岩石,食物来源,雨和不同颜色的斑点。这篇探讨性范文讲述了虚拟实验室的探讨。这些也可以生成随机模拟中的频率。所有的动画有一个能量水平,基本上一个动画为了生存就需要吃,渴时喝,避免闪电和岩石。我们可以说,如果他们成功了,他们就有认知系统，因为他们会知道如何生存。
动画可以留下痕迹,并观察他们的轨迹。我们可以欣赏虚拟环境，我们的详细技术可以描述虚拟实验室的功能。有许多模型,解决生存的问题是这样的环境。下面的范文进行详述。
Following the ideas presented in Gershenson, González, and Negrete (2017), I developed a virtual laboratory for testing the performance of animats controlled by mechanisms proposed from different perspectives2 in a simple virtual environment. Programmed in Java with the aid of Java3D libraries, this software is available to the public, source code and documentation.In my virtual laboratory, the user can create different phenomena, such as rocks (grey cubes), food sources (green spheres), rain (blue semitransparent cylinders), lightnings (black cylinders), and spots of different colours (circles): randomly or in specific positions. These also can be generated randomly during the simulation at a selected frequency.
Lightnings turn into rain after ten time steps, and rain turns into food after fifty time steps. All the animats have an energy level, which decreases when their hunger or thirst are high, and is increased when these are low (energy, thirst, hunger 0 [0..1]). An animat dies if its energy is exhausted. Eating food decreases their hunger. They can decrease their thirst by drinking under rains. Hunger and thirst are increased if they attempt to drink or eat “incorrect” stimuli. They lose energy if they touch lightnings or rocks. Basically, an animat in order to survive just needs to eat when hungry, drink when thirsty, and avoid lightnings and rocks. We can say that they are cognitive systems if they are successful, because they would know how to survive. The animats can leave a coloured trail in order to observe their trajectories. We can appreciate screenshots of the virtual environment in Figure 1 and Figure 2. A detailed technical description of the implementation of my virtual laboratory can be found in Appendix B.
There are many models that would solve the problem of surviving is such an environment, but I decided to implement representative models of different paradigms in order to observe their differences and similitudes. These models are as follows: a rule-based system typical of traditional knowledge-based and expert systems (e.g. Newell and Simon, 1972); Maes’ (1990, 1991) action selection mechanism, an already classical behaviour-based system (Brooks, 1986; Maes, 1994); my original architecture of recursive concepts as an example of the novel concept-based approach (Gärdenfors, 2017); a simple feed-forward artificial neural network (for an introduction, see Arbib, 1995); and a Braitenberg-style architecture (Braitenberg, 1984).
Rule-based animats
Rule-based animats have perceptual and motor functions that ignore the problems of implementing perception and motion in physical agents, focussing only in the control mechanism. They are inspired in classical knowledge-based systems (Newell and Simon, 1972; Newell, 1990), which use logic rules manipulating symbols in order to control a system. Table 1 shows the rules that control the animats in their environment. If rule-based animats perceive a rock near them, they avoid it. If they are thirsty and perceive rain, then if they can reach it drink, otherwise approach to it, and so on.
They approach lightnings when thirsty and rains when hungry because “they know” that these will turn into rain and food respectively. The perceptual system detects phenomena in all directions at a distance lesser than the adjustable animat’s radius of perception, detects phenomena near when they are at a distance lesser than the radius of the animat’s body, and detects phenomena at range when the animat touches them. The motor system approaches phenomena in a straight line, explores with random movements, and avoids obstacles semi-randomly turning about / or - 90 degrees. Knowledge-based systems have several limitations (e.g. see Maes, 1994), and are not optimal for implementing different types of system, but they are suitable for this task. We could see them as models of cognizers, even humans, which in such conditions would deliberately take those decisions. Classical cognitive science argues that humans are cognitive systems because they use rules and reasoning as the ones these animats could model (e.g. Newell, 1990).
Behaviour-based animats
Maturana and Varela give the following definition: “behaviour is a description an observer makes of the changes in a system with respect to an environment with which the system interacts” (Maturana and Varela, 1987, p. 163). We should just remember that behaviours are defined by an observer. Behaviour-basedsystems(Brooks, 1986; Maes, 1994) have been inspired in ethology for modelling adaptive behaviour and building adaptive autonomous agents. In problem domains where the system needs to be adaptive, they have several advantages over knowledge-based systems (Maes, 1994).
But when it comes to modelling the type of cognition that knowledgebased systems model, they have not produced any better results (Kirsh, 1991; Gershenson, 2017b). I implemented Maes’ (1990; 1991) action selection mechanism (ASM) for controlling my behaviour-based animats. It consists of a network of behaviours. Each behaviour has an activation level, a threshold, and a set of conditions in order to be “executable”. An executable behaviour whose activation level surpasses the threshold becomes active. The creatures controlled by Maes’ ASM also have motivations such as hunger or safety, which also contribute to the activation of behaviours. The behaviours are connected through “predecessor”, “successor”, and “conflicter” links. There is a predecessor link between A and B (B precedes A) if B makes certain conditions of A come true. For example, “eat” has “approach food” as a predecessor. There is a matching successor link in the opposite direction for every predecessor link.
There is a conflicter link from A to B if B makes a condition of A undone. Behaviours activate and inhibit each other, so after some time the “best” behaviour becomes selected. For details of this ASM, the reader is referred to Maes (1990; 1991). As with the rule-based animats, here I also simplify perceptual and motor systems (actually using the same systems already described), since they take the form of procedures such as “food perceived” or “avoid obstacle”, and these systems have to deal with the problem of distinguishing food from non-food, or how to move in order not to crash. Figure 3 shows the behaviour network of the animats. The external conditions of the behaviours are obvious: rock or lightning near for “avoid” , food perceived for “approach food”, food at range for “eat”, none for “explore”, etc.
The internal motivations are safety (constant) for “avoid”, hunger for the ones related with food, thirst for the ones related with thirst, none for “approach lightning”, hunger, thirst and curiosity (constant) for “explore”, and boredom (constant) for “none”. Curiously, the animats, even when they make discriminations between the highest motivations (approach food if more hungry than thirsty even if rain is closer), behave in a reactive way (eat when they are not hungry), because of the activation of behaviours by the successor links. Therefore, if the behaviour “none” is active for a long time, it will increase the value of “explore”, and this “approach rain” if this one is present, because thirst is not a direct condition. Of course, I could fix Maes’ ASM adding the motivations as a condition for a behaviour to become executable, or explore exhaustively the parameter space of the mechanism. This last option is not an easy one, since indeed it takes some time to adjust all the parameters by trial-and-error.
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