Like any other logical theory, domain descriptions in reasoning about actions may evolve, and thus need revision methods to adequately accommodate new information about the behavior of actions. The present work is about changing action domain descriptions in propositional dynamic logic. Its contribution is threefold: first we revisit the semantics of action theory contraction that has been done in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness w.r.t. our semantics. Finally we state postulates for action theory contraction and assess the behavior of our operators w.r.t. them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for contraction.
Liberatore @cite_37 proposes a framework for reasoning about actions in which it is possible to express a given semantics of belief update, like Winslett's @cite_42 and Katsuno and Mendelzon's @cite_1 . This means it is the formalism, essentially an action description language, that is used to describe updates (the change of propositions from one state of the world to another) by expressing them as laws in the action theory. The main difference between Liberatore's work and Li and Pereira's is that, despite not being concerned, at least a priori, with changing action laws, Liberatore's framework allows for abductively introducing in the action theory new effect propositions (effect laws, in our terms) that consistently explain the occurrence of an event.
@cite_37 @cite_42 @cite_1
2029617471 1984302133 2206132849 2250680464
The Katsuno and Mendelzon theory of belief update has been proposed as a reasonable model for revising beliefs about a changing world. However, the semantics of update relies on information which is not readily available. We describe an alternative semantical view of update in which observations are incorporated into a belief set by: a) explaining the observation in terms of a set of plausible events that might have caused that observation; and b) predicting further consequences of those explanations. We also allow the possibility of conditional explanations. We show that this picture naturally induces an update operator under certain assumptions. However, we argue that these assumptions are not always reasonable, and they restrict our ability to integrate update with other forms of revision when reasoning about action. Abstract Belief revision and belief update are two different forms of belief change, and they serve different purposes. In this paper we focus on belief update, the formalization of change in beliefs due to changes in the world. The complexity of the basic update (introduced by Winslett, 1990) has been determined by Eiter and Gottlob (1992). Since then, many other formalizations have been proposed to overcome the limitations and drawbacks of Winslett's update. In this paper we analyze the complexity of the proposals presented in the literature: the standard semantics by Winslett (1986), the minimal change with exception and the minimal change with maximal disjunctive information by Zhang and Foo (1996), the update with disjunctive function by Herzig (1996), the abduction-based update and the generalized update by Boutilier (1996). We relate some of these approaches to belief update to previous work on closed world reasoning. This paper studies an abduction problem in formal argumentation frameworks. Given an argument, an agent verifies whether the argument is justified or not in its argumentation framework. If the argument is not justified, the agent seeks conditions to explain the argument in its argumentation framework. We formulate such abductive reasoning in argumentation semantics and provide its computation in logic programming. Next we apply abduction in argumentation frameworks to reasoning by players in debate games. In debate games, two players have their own argumentation frameworks and each player builds claims to refute the opponent. A player may provide false or inaccurate arguments as a tactic to win the game. We show that abduction is used not only for seeking counter-claims but also for building dishonest claims in debate games. Knowledge about how the world changes over time is a vital component of commonsense knowledge for Artificial Intelligence (AI) and natural language understanding. Actions and events are fundamental components to any knowledge about changes in the state of the world: the states before and after an event differ in regular and predictable ways. We describe a novel system that tackles the problem of extracting knowledge from text about how actions and events change the world over time. We leverage standard language-processing tools, like semantic role labelers and coreference resolvers, as well as large-corpus statistics like pointwise mutual information, to identify STRIPS representations of actions and events, a type of representation commonly used in AI planning systems. In experiments on Web text, our extractor’s Area under the Curve (AUC) improves by more than 31 over the closest system from the literature for identifying the preconditions and add effects of actions. In addition, we also extract significant aspects of STRIPS representations that are missing from previous work, including delete effects and arguments.