We present a multi-modal action logic with first-order modalities, which contain terms which can be unified with the terms inside the subsequent formulas and which can be quantified. This makes it possible to handle simultaneously time and states. We discuss applications of this language to action theory where it is possible to express many temporal aspects of actions, as for example, beginning, end, time points, delayed preconditions and results, duration and many others. We present tableaux rules for a decidable fragment of this logic.
Javier Pinto has extended situation calculus in order to integrate time @cite_3 . He conserves the framework of situation calculus and introduces a notion of time. Intuitively, every situation @math has a starting time and an ending time, where @math meaning that situation @math ends when the succeeding situation @math is reached. The end of the situation @math is the same time point as the beginning of the next situation resulting from the occurrence of action @math in @math . The obvious asymmetry of the @math and @math functions is due to the fact that the situation space has the form of a tree whose root is the beginning state @math . Thus, every state has a unique preceding state but eventually more that one succeeding state.
@cite_3
1992170615 2114462967 2134275439 2133027741
The Situation Calculus is a logic of time and change in which there is a distinguished initial situation and all other situations arise from the different sequences of actions that might be performed starting in the initial one. Within this framework, it is difficult to incorporate the notion of an occurrence, since all situations after the initial one are hypothetical. These occurrences are important, for instance, when one wants to represent narratives. There have been proposals to incorporate the notion of an action occurrence in the language of the Situation Calculus, namely Miller and Shanahan’s work on narratives [22] and Pinto and Reiter’s work on actual lines of situations [27, 29]. Both approaches have in common the idea of incorporating a linear sequence of situations into the tree described by theories written in the Situation Calculus language. Unfortunately, several advantages of the Situation Calculus are lost when reasoning with a narrative line or with an actual line of occurrences. In this paper we propose a different approach to dealing with action occurrences and narratives, which can be seen as a generalization of narrative lines to narrative trees. In this approach we exploit the fact that, in the discrete Situation Calculus [13], each situation has a unique history. Then, occurrences are interpreted as constraints on valid histories. We argue that this new approach subsumes the linear approaches of Miller and Shanahan’s, and Pinto and Reiter’s. In this framework, we are able to represent various kinds of occurrences; namely, conditional, preventable and non-preventable occurrences. Other types of occurrences, not discussed in this article, can also be accommodated. Formal Logic can be used to represent knowledge of many kinds for many purposes. It can be used to formalize programs, program specifications, databases, legislation, and natural language in general. For many such applications of logic a representation of time is necessary. Although there have been several attempts to formalize the notion of time in classical first-order logic, it is still widely believed that classical logic is not adequate for the representation of time and that some form of non-classical Temporal Logic is needed. In this paper, we shall outline a treatment of time, based on the notion of event, formalized in the Horn clause subset of classical logic augmented with negation as failure. The resulting formalization is executable as a logic program. We use the term ‘‘event calculus’’ to relate it to the well-known ‘‘situation calculus’’ (McCarthy and Hayes 1969). The main difference between the two is conceptual: the situation calculus deals with global states whereas the event calculus deals with local events and time periods. Like the event calculus, the situation calculus can be formalized by means of Horn clauses augmented with negation by failure (Kowalski 1979). The main intended applications investigated in this paper are the updating of databases and narrative understanding. In order to treat both cases uniformly we have taken the view that an update consists of the addition of new knowledge to a knowledge base. The effect of explicit deletion of information in conventional databases is obtained without deletion by adding new knowledge about the end of the period of time for which the information holds. John McCarthy's situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change. These lecture notes highlight the mathematical and computational structure relating to the formulation of, and development of algorithms for, the Bayesian approach to inverse problems in differential equations. This approach is fundamental in the quantification of uncertainty within applications in volving the blending of mathematical models with data. The finite dimensional situation is described first, along with some motivational examples. Then the development of probability measures on separable Banach space is undertaken, using a random series over an infinite set of functions to construct draws; these probability measures are used as priors in the Bayesian approach to inverse problems. Regularity of draws from the priors is studied in the natural Sobolev or Besov spaces implied by the choice of functions in the random series construction, and the Kolmogorov continuity theorem is used to extend regularity considerations to the space of Holder continuous functions. Bayes’ theorem is de rived in this prior setting, and here interpreted as finding conditions under which the posterior is absolutely continuous with respect to the prior, and determining a formula for the Radon-Nikodym derivative in terms of the likelihood of the data. Having established the form of the posterior, we then describe various properties common to it in the infinite dimensional setting. These properties include well-posedness, approximation theory, and the existence of maximum a posteriori estimators. We then describe measure-preserving dynamics, again on the infinite dimensional space, including Markov chain-Monte C arlo and sequential Monte Carlo methods, and measure-preserving reversible stochastic differential equations. By formulating the theory and algorithms on the underlying infinite dimensional space, we obtain a framework suitable for rigorous analysis of the accuracy of reconstructions, of computational complexity, as well as naturally constructing algorithms which perform well under mesh refinement, since they are inherently well-defined in infinite dimensions.