A central issue in the treatment of attitudes is that of logical omniscience: humans are cognitively limited agents, and are not aware of all the consequences of their beliefs and desires. On this basis, Kamp (1990) argues that the objects of thought should not be seen as purely model-theoretic entities, for example a set of belief worlds, as is common in logics of belief from Hintikka (1962) onwards. Rather, the logic of belief and other attitudes must involve structured mental representations. The problem of logical omniscience is then resolved in terms of the failure of human agents to perform logically complete computations over these representations. Modeling the objects of attitudes in this way, as representational entities, allows us to make the fine distinctions necessary to distinguish between believing that it is raining and believing that if it is not raining then 37 is not prime. However, traditional versions of such a sententialist or Language-of-Thought approach to omniscience typically run into the problem of intentionality: to be useful to us, our attitudes must somehow be about the external world we live in, so we still have to explain how these syntactic representations connect with reality. In a Hintikka-style possible worlds semantics the link between world and attitude is hardwired into the semantics: believing that John is happy means that the agent takes the actual world to be an element of the set of worlds where John is happy. The DRT approach combines these two strategies: use the classic, model-theoretic semantics of the DRS language to cash out intentionality in terms of possible worlds, but hold on to the representational level to model resource-bounded computations in practical reasoning.