Traditionally, consistency is the only criterion for the quality of a theory in logic-based approaches to reasoning about actions. This work goes beyond that and contributes to the meta-theory of actions by investigating what other properties a good domain description should satisfy. Having Propositional Dynamic Logic (PDL) as background, we state some meta-theoretical postulates <br />concerning this sore spot. When all postulates are satisfied, we call the action theory modular. We point out the problems that arise when the postulates about modularity are violated, and propose algorithmic checks that can help the designer of an action theory to overcome them. Besides being easier to understand and more elaboration tolerant in McCarthy's sense, modular theories<br />have interesting computational properties. Moreover, we also propose a framework for updating domain descriptions and show the importance modularity has in action theory change.

This dissertation is about the design of a modular language for describing actions. The modular action description language, MAD, is based on the action language C+. In this new language, the possibility of "importing" a module allows us to describe actions by referring to descriptions of related actions introduced earlier, rather than by listing all effects and preconditions of every action explicitly. The use of modular action descriptions eliminates the need to reinvent theories of similar domains over and over again. Another advantage of this representation style is that it is similar to the way humans describe actions in terms of other actions. We first define the syntax of a fragment of MAD, called mini-MAD, and then extend it to the full version of MAD. The semantics of mini-MAD is defined by grounding action descriptions and translating them into C+. However, for the full version of MAD, it would be difficult to define grounding. Instead, we use a new approach to the semantics of variables in action descriptions, which is based on more complex logical machinery---first-order causal logic. Grounding is important as an implementation method, but we argue that it should be best avoided in the definition of the semantics of expressive action languages. We show that, in application to mini-MAD, the two semantics are equivalent. Furthermore, we prove that MAD action descriptions have some desirable, intuitively expected mathematical properties. We hope that MAD will make it possible to create a useful general-purpose library of standard action descriptions and will contribute in this way to solving the problem of generality in Artificial Intelligence. / text

<p>This master's thesis deals with a frame-based knowledge representa- tion language and system called The Knowledge Machine (KM), de- veloped by Peter Clark and Bruce Porter at the University of Texas at Austin. The purpose of the thesis is to show a number of ways of changing and extending KM to handle larger classes of reasoning tasks associated with reasoning about actions and change.</p>

abstract: Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former. / Dissertation/Thesis / Ph.D. Computer Science 2012

This master's thesis deals with a frame-based knowledge representa- tion language and system called The Knowledge Machine (KM), de- veloped by Peter Clark and Bruce Porter at the University of Texas at Austin. The purpose of the thesis is to show a number of ways of changing and extending KM to handle larger classes of reasoning tasks associated with reasoning about actions and change.

The capability of changing beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. Belief change therefore is one of the central research fields in Artificial Intelligence (AI) for over two decades. In the AI literature, two different kinds of belief change operations have been intensively investigated: belief update, which deal with situations where the new information describes changes of the world; and belief revision, which assumes the world is static. As another important research area in AI, reasoning about actions mainly studies the problem of representing and reasoning about effects of actions. These two research fields are closely related and apply a common underlying principle, that is, an agent should change its beliefs (knowledge) as little as possible whenever an adjustment is necessary. This lays down the possibility of reusing the ideas and results of one field in the other, and vice verse. This thesis aims to develop a general framework and devise computational models that are applicable in reasoning about actions. Firstly, I shall propose a new framework for iterated belief revision by introducing a new postulate to the existing AGM/DP postulates, which provides general criteria for the design of iterated revision operators. Secondly, based on the new framework, a concrete iterated revision operator is devised. The semantic model of the operator gives nice intuitions and helps to show its satisfiability of desirable postulates. I also show that the computational model of the operator is almost optimal in time and space-complexity. In order to deal with the belief change problem in multi-agent systems, I introduce a concept of mutual belief revision which is concerned with information exchange among agents. A concrete mutual revision operator is devised by generalizing the iterated revision operator. Likewise, a semantic model is used to show the intuition and many nice properties of the mutual revision operator, and the complexity of its computational model is formally analyzed. Finally, I present a belief update operator, which takes into account two important problems of reasoning about action, i.e., disjunctive updates and domain constraints. Again, the updated operator is presented with both a semantic model and a computational model.

The capability of changing beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. Belief change therefore is one of the central research fields in Artificial Intelligence (AI) for over two decades. In the AI literature, two different kinds of belief change operations have been intensively investigated: belief update, which deal with situations where the new information describes changes of the world; and belief revision, which assumes the world is static. As another important research area in AI, reasoning about actions mainly studies the problem of representing and reasoning about effects of actions. These two research fields are closely related and apply a common underlying principle, that is, an agent should change its beliefs (knowledge) as little as possible whenever an adjustment is necessary. This lays down the possibility of reusing the ideas and results of one field in the other, and vice verse. This thesis aims to develop a general framework and devise computational models that are applicable in reasoning about actions. Firstly, I shall propose a new framework for iterated belief revision by introducing a new postulate to the existing AGM/DP postulates, which provides general criteria for the design of iterated revision operators. Secondly, based on the new framework, a concrete iterated revision operator is devised. The semantic model of the operator gives nice intuitions and helps to show its satisfiability of desirable postulates. I also show that the computational model of the operator is almost optimal in time and space-complexity. In order to deal with the belief change problem in multi-agent systems, I introduce a concept of mutual belief revision which is concerned with information exchange among agents. A concrete mutual revision operator is devised by generalizing the iterated revision operator. Likewise, a semantic model is used to show the intuition and many nice properties of the mutual revision operator, and the complexity of its computational model is formally analyzed. Finally, I present a belief update operator, which takes into account two important problems of reasoning about action, i.e., disjunctive updates and domain constraints. Again, the updated operator is presented with both a semantic model and a computational model.