Representing defeasibility is an important issue in common sense reasoning. In reasoning about action and change, this issue becomes more difficult because domain and action related defeasible information may conflict with general inertia rules. Furthermore, different types of defeasible information may also interfere with each other during the reasoning. In this paper, we develop a prioritized logic programming approach to handle defeasibilities in reasoning about action. In particular, we propose three action languages AT ^ 0 , AT ^ 1 and AT ^ 2 which handle three types of defeasibilities in action domains named defeasible constraints, defeasible observations and actions with defeasible and abnormal effects respectively. Each language with a higher superscript can be viewed as an extension of the language with a lower superscript. These action languages inherit the simple syntax of A language but their semantics is developed in terms of transition systems where transition functions are defined based on prioritized logic programs. By illustrating various examples, we show that our approach eventually provides a powerful mechanism to handle various defeasibilities in temporal prediction and postdiction. We also investigate semantic properties of these three action languages and characterize classes of action domains that present more desirable solutions in reasoning about action within the underlying action languages.
Although the work presented in @cite_2 provided a natural way to represent causality in reasoning about action, there were several restrictions in this action theory. First, due to technical restrictions, only normal defaults or defaults without justifications are the suitable forms to represent causal rules in problem domains. Second, this action theory did not handle the other two major defeasibilities - defeasible observations and actions with defeasible and abnormal effects.
@cite_2
2017479918 2150014546 204991199 2321919568
Recent research on reasoning about action has shown that the traditional logic form of domain constraints is problematic to represent ramifications of actions that are related to causality of domains. To handle this problem properly, as proposed by some researchers, it is necessary to describe causal relations of domains explicitly in action theories. In this paper, we address this problem from a new point of view. Specifically, unlike other researchers viewing causal relations as some kind of inference rules, we distinguish causal relations between defeasible and non-defeasible cases. It turns out that a causal theory in our formalism can be specified by using Reiter's default logic. Based on this idea, we propose a causality-based minimal change approach for representing effects of actions, and argue that our approach provides more plausible solutions for the ramification and qualification problems compared with other related work. We also describe a logic programming approximation to compute causal theories of actions which provides an implementational basis for our approach. Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. We extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent on a set of structural and temporal rules. We designed animated representations, based on Michotte's rules, for showing complex causal relationships or causal semantics. In this paper we describe a set of animations for showing semantics such as causal amplification, causal strength, causal dampening, and causal multiplicity. In a two part study we compared the effectiveness of both the static and animated representations. The first study (N=44) asked participants to recall passages that were previously displayed using both types of representations. Participants were 8 more accurate in recalling causal semantics when they were presented using animations instead of static graphs. In the second study (N=112) we evaluated the intuitiveness of the representations. Our results showed that while users were as accurate with the static graphs as with the animations, they were 9 faster in matching the correct causal statements in the animated condition. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations. We address the problem of visually detecting causal events and fitting them together into a coherent story of the action witnessed by the camera. We show that this can be done by reasoning about the motions and collisions of surfaces, using high-level causal constraints derived from psychological studies of infant visual behavior. These constraints are naive forms of basic physical laws governing substantiality, contiguity, momentum, and acceleration. We describe two implementations. One system parses instructional videos, extracting plans of action and key frames suitable for storyboarding. Since learning will play a role in making such systems robust, we introduce a new framework for higher-order hidden Markov models and demonstrate its use in a second system that segments stereo video into actions in near real-time. Rather than attempt accurate low-level vision, both systems use high-level causal analysis to integrate fast but sloppy pixel-based representations over time. The output is suitable for summary, indexing, and automated editing. Causal reasoning plays an essential role in both informal and formal human decision-making. Causality itself as well as human understanding of causality is imprecise, sometimes necessarily so. A common sense understanding of the world tells us that we have to deal with imprecision, uncertainty and imperfect knowledge. A difficulty is striking a good balance between precise formalism and commonsense imprecise reality. An algorithmic method of accommodating imprecision in causality is needed. Today, data mining holds the promise of extracting unsuspected information from very large databases. However, the most common data mining rule forms do not express a causal relationship. Without understanding the underlying causality, a naive use of data mining rules can lead to undesirable actions.