We pursue the perspective of Reiter that in the situation calculus one can formalize primitive, determinate actions with axioms which, among others, include two disjoint sets: a set of successor state axioms and a set of action precondition axioms. We posed ourselves the problem of automatically generating successor state axioms, given only a set of effect axioms and a set of state constraints. This is a special version of what has been traditionally called the ramification problem. To our surprise, we found that there are state constraints whose role is not to yield indirect effects of actions. Rather, they are implicit axioms about action preconditions. As such, they are intimately related to the classical qualification problem. We also discovered that other kinds of state constraints arise; these are related to the formalization of strategic or control information. This paper is devoted to describing our results along these lines, focusing on ramification and qualification state con...

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything useful about the causal mechanisms that underly the observations. We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we address the issue of non-temporal causation. 1 Introduction The study of causation is central to the understanding of hum...

This paper considers the problem of specifying the effects of actions in the situation calculus using domain constraints. We argue that normal state constraints that refer to only the truth values of fluents are not strong enough for this purpose, and that a notion of causation needs to be employed explicitly. Technically, we introduce a new ternary predicate Caused into the situation calculus: Caused(p; v; s) if the proposition p is caused (by something unspecified) to have the truth value v in the state s. Using this predicate, we can represent not only action-triggered causal statements such as that the action load causes the gun to be loaded, but also fluent-triggered ones such as that the fact that the switch is in the up position causes the lamp to be on. The former is convenient for representing the direct effects of actions, and the latter the indirect effects. 1 Introduction We consider the problem of formalizing the effects of actions in the situation calculus [ McCarthy a...

. The paper is concerned with the succinct axiomatization and ecient deduction of non-change, within McCarthy and Hayes' Situation Calculus. The idea behind the proposed approach is this: suppose that in a room containing a man, a robot and a cat as the only potential agents, the only action taken by the man within a certain time interval is to walk from one place to another, while the robot's only actions are to pick up a box containing the (inactive) cat and carry it from its initial place to another. We wish to prove that a certain object (such as the cat, or the doormat) did not change color. We reason that the only way it could have changed color is for the man or the robot to have painted or dyed it. But since these are not among the actions which actually occurred, the color of the object is unchanged. Thus we need no frame axioms to the eect that walking and carrying leave colors unchanged (which is in general false in multi-agent worlds), and no default schema that properties change only when we can prove they do (which is in general false in incompletely known worlds). Instead we use explanationclosure axioms specifying all primitive actions which can produce a given type of change within the setting of interest. A method similar to this has been proposed by Andrew Haas for singleagent, serial worlds. The contribution of the present paper lies in 1 showing (1) that such methods do indeed encode non-change succinctly, (2) are independently motivated, (3) can be used to justify highly ecient methods of inferring non-change, specically the \sleeping dog" strategy of STRIPS, and (4) can be extended to simple multiagent worlds with concurrent actions. An ultimate limitation may lie in the lack of a uniform strategy for deciding what ...

Reasoning about change is an important aspect of commonsense reasoning and planning. In this paper we describe an approach to reasoning about change for rich domains where it is not possible to anticipate all situations that might occur. The approach provides a solution to the frame problem, and to the related problem that it is not always reasonable to explicitly specify all of the consequences of actions. The approach involves keeping a single model of the world that is updated when actions are performed. The update procedure involves constructing the nearest world to the current one in which the consequences of the actions under consideration hold. The way we find the nearest world is to construct proofs of the negation of the explicit consequences of the expected action, and to remove a premise in each proof from the current world. Computationally, this construction procedure appears to be tractable for worlds like our own where few things tend to change with each action, or where ...

This paper presents an approach to temporal reasoning in which prediction is deduction but explanation is abduction. It is argued that all causal laws should be expressed in the natural form effect if cause. Any given set of laws expressed in this way can be used for both forwards projection (prediction) and backwards projection (explanation), but abduction must be used for explanation whilst deduction is used for prediction. The approach described uses a shortened form of Kowalski and Sergot's Event Calculus and incorporates the assumption that properties known to hold must have explanations in terms of events. Using abduction to implement this assumption results in a form of default persistence which correctly handles problems which have troubled other formulations. A straightforward extension to SLD resolution is described which implements the abductive approach to explanation, and which complements the well-understood deductive methods for prediction. Introduction Temporal reason...

Most of the solutions proposed to the Yale shooting problem have either introduced new nonmonotonic reasoning methods (generally involving temporal priorities) or completely reformulated the domain axioms to represent causality explicitly. This paper presents a new solution based on the idea that since the abnormality predicate takes a situational argument, it is important for the meanings of the situations to be held constant across the various models being compared. This is accomplished by a simple change in circumscription policy: when Ab is circumscribed, Result (rather than Holds) is allowed to vary. In addition, we need an axiom ensuring that every consistent situation is included in the domain of discourse. Ordinary circumscription will then produce the intuitively correct answer. Beyond its conceptual simplicity, the solution proposed here has additional advantages over the previous approaches. Unlike the approach that uses temporal priorities, it can support reasoning backward...

A narrative is a course of real events about which we might have incomplete information. Formalisms for reasoning about action may be broadly divided into those which are narrativebased, such as the Event Calculus of Kowalski and Sergot, and those which reason on the level of hypothetical sequences of actions, in particular the Situation Calculus. This paper bridges the gap between these types of formalism by supplying a technique for linking incomplete narrative descriptions to Situation Calculus domain formulae written in the usual style using a Result function. Particular attention is given to actions with duration and overlapping actions. By illuminating the relationship between these two different styles of representation, the paper moves us one step closer to a full understanding of the space of all possible formalisms for reasoning about action. Introduction The Situation Calculus [15] is one of A.I.'s oldest and best understood formalisms for representing change, but it has of...

It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...