Most standard results on structure identification in first order theories depend upon the correctness and completeness (in the limit) of the data, which are provided to the learner. These assumption are essential for the reliability of inductive methods and for their limiting success (convergence to the truth). The paper investigates inductive inference from (possibly) incorrect and incomplete data. It is shown that such methods can be reliable not in the sense of truth approximation, but in the sense that the methods (...) converge to "empirically adequate" theories, i.e. theories, which are consistent with all data (past and future) and complete with respect to a given complexity class of L-sentences. Adequate theories of bounded complexity can be inferred uniformly and effectively by polynomial-time learning algorithms. Adequate theories of unbounded complexity can be inferred pointwise by less efficient methods. (shrink)

Peirce is often credited with having formulated a pragmatic theory of truth. This can be misleading, if it is assumed that Peirce was chiefly interested in providing a metaphysical analysis of the immediate conditions under which a belief or proposition is true, or the conditions under which a proposition or belief is said to be madetrue. Cheryl Misak has exposed the subtleties in Peirce's discussion of truth, especially showing the difficulties faced by any ascription to him of an analytic definition (...) of truth. In this paper I follow Misak in urging that Peirce's contribution to the philosophical discussion about the nature of truth was not of that kind. What makes his pragmatic approach distinctive is that rather than attempting to state the nature of truth per se, it attempts to uncover the beliefs and expectations we commit ourselves to when we make specific claims that such and such is true or is the case. (shrink)