Structural analogies connect Williamson’s epistemology and action theory: for example, action is the direction-of-fit mirror image of knowledge, and knowledge stands to belief as action stands to intention. These structural analogies, for Williamson, are meant to illuminate more generally how ‘mirrors’ reversing direction of fit should be understood as connecting the spectrum of our cognitive and practically oriented mental states. This paper has two central aims, one negative and the other positive. The negative aim is to highlight some intractable problems (...) with Williamson’s preferred analogical picture, which links the cognitive and the practical through the nexus of direction-of-fit mirroring. The positive aim of the paper is to propose a better alternative. In particular, we show that an achievement-theoretic proposal captures what is in common across the range of attitudes that exhibit the kind of structure that knowledge-belief, action-desire/intention do, while at the same time avoiding the problems shown to face Williamson’s proposed picture. Moreover, we draw attention to several key theoretical benefits of embracing our proposed achievement-theoretic picture, including some of the key benefits of the knowledge-first programme that Williamson’s own analogies were designed to secure. (shrink)

I extend my direct virtue epistemology to explain how a knowledge-first framework can account for two kinds of positive epistemic standing, one tracked by externalists, who claim that the virtuous duplicate lacks justification, the other tracked by internalists, who claim that the virtuous duplicate has justification, and moreover that such justification is not enjoyed by the vicious duplicate. It also explains what these kinds of epistemic standing have to do with each other. I argue that all justified beliefs are good (...) candidates for knowledge, and are such because they are exercises of competences to know. However, there are two importantly different senses in which a belief may be a good candidate for knowledge, one corresponding to an externalist kind of justification and the other corresponding to an internalist one. I show how the account solves the new evil demon problem in a more satisfactory way than existing accounts. We end up with a view of knowledge, justification, and rationality that is plausible, motivated, and theoretically unified. (shrink)

The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...) that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)