Bayesian issues in ant navigation

我们的大脑必须处理歧义和不确定性，而对它们如何做到这一点的一种越来越流行的解释是基于贝叶斯推理。从本质上讲，这意味着我们根据当前的感官输入(这看起来像我的房子)和之前的预期(考虑到我的起点位置和行驶速度，我现在还不指望到家)来估计发生某种情况的可能性(比如‘我在家’)。贝叶斯定理告诉我们，我们应该如何结合这些因素，以获得对我们当前状态的最佳估计。但是，这种形式的推理是普遍的吗？研究这一问题的理想方法是观察那些必须解决类似问题的“简单”动物。而测试我们对这些动物所做事情的理解的一个有效方法是在同样的感知环境中运行的机器人模型中实施和测试我们的假设。解决这类问题的动物的一个明显例子是沙漠蚂蚁，它们单独觅食，不使用化学痕迹，但可以在贫瘠或复杂的环境中有效地远距离重新安置它们的巢穴或食物来源。最近的研究表明，蚂蚁可以通过杂乱的环境单独学习和回忆特定的路线，这些环境迫使蚂蚁绕道并阻止使用遥远的地标。蚂蚁导航依赖于两个主要机制：它们可以跟踪它们已经移动了多远，以及从蚁穴向哪个方向移动，并不断更新指向家乡的矢量；它们可以识别熟悉的视觉环境，并使用这些来确定前进的方向。他们是否以最佳方式整合了这些线索？如果一条或另一条球杆或多或少是可变的怎么办？他们能用这些线索中的一个来消除另一个的歧义吗？通过借鉴为机器人导航开发的方法，我们可以使这些问题的研究变得严谨和定量。我们将首先通过跟踪蚂蚁觅食，并从蚂蚁眼睛的角度捕捉图像，来确定蚂蚁开发新路线时实际看到的是什么。我们将把这些信息输入算法，这些算法应该能够学习该地区的地图。我们可以系统地改变可用的信息类型、其可靠性和用于更新地图的计算方法，并将其性能与蚂蚁进行比较。进一步的实验，看看当同样的变量被操纵时，蚂蚁会做些什么，这将有助于评估这些模型。最后，这些模型还将在现实世界中进行测试，方法是在一个能够在蚂蚁环境中导航的小型机器人上实现它们。

Our brains have to deal with ambiguity and uncertainty, and an increasingly popular explanation of how they do so is based on Bayesian reasoning. In essence, this says we estimate the probability of a certain state of affairs (such as 'I am at home') on the basis of both current sensory inputs ('This looks like my house') and prior expectations ('Given my starting location, and the speed I was travelling, I wouldn't expect to be home yet'). Bayes theorem tells us how we should combine these factors to obtain the best estimate of our current state. But is this form of reasoning universal? An ideal way to investigate this issue is to look at 'simple' animals that have to solve analogous problems. And an effective way to test our understanding of what these animals do is to implement and test our hypotheses in robot models that operate in the same sensory environment. A clear example of an animal solving such problems is found in desert ants, who forage individually and without the use of chemical trails, yet can efficiently relocate their nest or a food source over long distances in barren or complex environments. Recent studies have shown that ants can individually learn and recall specific routes through cluttered environments that force detours and prevent the use of distant landmarks. Ant navigation depends on two main mechanisms: they can keep track of how far they have moved and in which direction from the nest and continuously update a vector that points back home; and they can recognise familiar visual surroundings and use these to determine which way to go. Do they integrate these cues in an optimal fashion? What if one or other cue is more or less variable? Can they use one of these cues to disambiguate the other? We can make the investigation of these issues rigorous and quantitative by drawing on methods developed for robot navigation. We will first determine what ants actually see as they develop new routes, by following ants as they forage, and capturing images from the ant's eye point of view. We will feed this information into algorithms that should be able to learn a map of the area. We can systematically vary the type of information available, its reliability, and the computational methods used to update the map, and compare the performance to ants. Further experiments to see what the ants do when the same variables are manipulated will serve to evaluate the models. Finally, the models will also be tested in the real world by implementing them on a small robot able to navigate in the ant environment.