Fred meets Tweety Antonis Kakas 1 and Loizos Michael 2 and Rob Miller 3 Abstract. We propose a framework that brings together two major forms of default reasoning in Artificial Intelligence: applying default property classification rules in static domains, and default persistence of properties in temporal domains. Particular attention is paid to the problem of qualification, central in default reasoning and in any at- tempt to integrate different forms of this type of reasoning. We exam- ine previous semantics developed independently for the two separate forms of default reasoning, and illustrate how these naturally lead to the solution that we propose in integrating the two. The resulting inte- gration gives rise to domains where four different types of knowledge interact and qualify each other in an intricate manner. Through a se- ries of examples we show how this knowledge qualification leads to intuitive conclusions. We prove that our framework of integration is elaboration tolerant: extending a consistent domain with additional action occurrences, causal laws, or static knowledge does not render the domain inconsistent. The conclusions that are drawn are always adjusted so as to gracefully accommodate the extra knowledge. 1 Introduction Tweety is watching as we prepare to shoot Fred. We load the gun, we wait, and then shoot the gun. Will we conclude that Tweety will fly away as birds normally do when they hear a loud noise that shooting a loaded gun normally produces? It depends on whether Tweety can fly or not! If all we know about Tweety is that it is a bird, we then expect to see it flying, but if we also know that it is a penguin we will not expect to see it flying, even if we hear a loud noise produced by the act of firing. What can we conclude if after the act of shooting we observe that Tweety is still on the ground? That Tweety is not a typi- cal bird, or that the gun did not make a loud noise when fired, or even that the gun was not loaded at the time of shooting? Can we indeed conclude anything at all after such an unexpected observation? In this problem of “Fred meets Tweety” we need to bring together two major forms of default reasoning that have been extensively stud- ied on their own in A.I., but have rarely been addressed in the same formalism. These are default property classification as applied to in- heritance systems [5, 10], and default persistence central to temporal reasoning in theories of Reasoning about Action and Change (RAC) [4, 9, 11]. How can a formalism synthesize the reasoning encom- passed within each of these two forms of default reasoning? Central to these two (and indeed all) forms of default reasoning is the qualification problem: default conclusions are qualified by infor- mation that can block the application of the default inference. One aspect of the qualification problem is to express within the theory the 1 University of Cyprus, P. O. Box 20537, CY-1678, Cyprus. e-mail: antonis@ucy.ac.cy 2 Harvard University, Cambridge, MA 02138, U.S.A. e-mail: loizos@eecs.harvard.edu 3 University College London, London WC1E 6BT, U.K. e-mail: rsm@ucl.ac.uk knowledge required to properly qualify and block the default infer- ence under exceptional situations. This endogenous form of qualifi- cation is implicit in the theory, driven by auxiliary observations that enable the known qualifying information to be applied. For example, known exceptional classes in the case of default property inheritance, or known action laws (and their ramifications) in the case of default persistence, qualify respectively these two forms of default reasoning. But this task of completely representing within a given theory the qualification knowledge is impractical and indeed undesirable, as we want to jump to default conclusions based on a minimal set of infor- mation available. We, therefore, also need to allow for default con- clusions to be qualified unexpectedly from observed information that is directly (or explicitly) contrary to them. In this exogenous form of qualification the theory itself cannot account for the qualification of the default conclusion, but our observations tell us explicitly that this is so and we attribute the qualification to some unknown reason. Recent work [6, 12] has shown the importance for RAC theories to properly account for these two forms of qualification, so that an exogenous qualification is employed only when observations can- not be accounted for by an endogenous qualification of the causal laws and default persistence. In our problem of integrating the de- fault reasoning of property classification into RAC, this means that we need to ensure that the two theories properly qualify each other endogenously, so that the genuine cases of exogenous qualification can be correctly recognized. In particular, we study how a static de- fault theory expressing known default relationships between fluents can endogenously qualify the reasoning about actions and change, so that the application of causal laws and default persistence is properly adjusted by this static theory. In the Fred meets Tweety scenario de- scribed above, for example, the normal default that “penguins cannot fly” would act as an implicit qualification for the causal law that “a loud noise causes birds to fly”, but not so when either Tweety is not known to be a penguin, or it is known to be a super-penguin (super- penguins being an exception to the default that penguins cannot fly). More generally, we study how four different types of information present in such an integrated framework of RAC interact and qualify each other: (i) information generated by default persistence, (ii) ac- tion laws that qualify default persistence, (iii) static default laws of fluent relationships that can qualify these action laws, and (iv) obser- vations that can qualify any of these. This hierarchy of information comes full circle, as the bottom layer of default persistence of obser- vations (which carry the primary role of qualification) can also qual- ify the static theory. Hence, in our proposed integrated framework, temporal projection with the observations help to determine the ad- missible states of the static default theory. In turn, admissible states qualify the actions laws and the temporal projection they generate. Section 2 examines the qualification problem as studied in the two separate domains and its form for the proposed integration. Section 3 gives the formal semantics of the integration framework and the cen- tral result that ensures its elaboration tolerance. Section 4 briefly dis- In the procedings of CogRob'08: the 6th International Cognitive Robotics Workshop, July 21-22, 2008, Patras, Greece, www.cse.yorku.ca/cogrob08