Iterated belief change in the situation calculus

John McCarthy's situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change.     

A unifying action calculus

McCarthy's Situation Calculus is arguably the oldest special-purpose knowledge representation formalism, designed to axiomatize knowledge of actions and their effects. Four decades of research in this area have led to a variety of alternative formalisms: While some approaches can be considered instances or extensions of the classical Situation Calculus, like Reiter's successor state axioms or the Fluent Calculus, there are also special planning languages like ADL and approaches based on a linear (rather than branching) time structure like the Event Calculus. The co-existence of many different calculi has two main disadvantages: The formal relations among them is a largely open issue, and a lot of today's research concerns the transfer of specific results from one approach to another. In this paper, we present a unifying action calculus, which encompasses (well-defined classes of) all of the aforementioned formalisms. Our calculus not only facilitates comparisons and translations between specific approaches, it also allows to solve interesting problems for various calculi at once. We exemplify this by providing a general, calculus-independent solution to a problem of practical relevance, which is intimately related to McCarthy's quest for elaboration tolerant formalisms: the modularity of domain axiomatizations.     

Event calculus and temporal action logics compared

We compare the event calculus and temporal action logics (TAL), two formalisms for reasoning about action and change. We prove that, if the formalisms are restricted to integer time, inertial fluents, and relational fluents, and if TAL action type specifications are restricted to definite reassignment of a single fluent, then the formalisms are not equivalent. We argue that equivalence cannot be restored by using more general TAL action type specifications. We prove however that, if the formalisms are further restricted to single-step actions, then they are logically equivalent.     

John McCarthy's legacy

This special issue is dedicated to John McCarthy, founding father of Artificial Intelligence. It contains a collection of recent contributions to the field of knowledge representation and reasoning, a field that McCarthy founded and that has been a main focus of his research during the last half century. In this introductory article, we survey some of McCarthy's major contributions to the field of knowledge representation and reasoning, and situate the papers in this special issue in the context of McCarthy's previous work.     

DemaWare2: Integrating sensors,multimedia and semantic analysis for the ambient care of dementia

This paper presents DemaWare2, an Ambient Assisted Living framework to support the care of people with dementia. The framework integrates various sensor modalities, such as ambient, wearable, offline and cloud-based, together with sophisticated, interdisciplinary methods including image, audio and semantic analysis. Fine-grained, atomic events, such as object manipulation, are aggregated into complex activities through semantic fusion. Applications tailored to monitoring dementia symptoms support clinicians to drive effective, timely interventions and evaluate their outcomes. The framework was evaluated for its robustness, reliability and clinical value in real-world lab trials and home installations.     

A semantic data-driven knowledge base construction method to assist designers in design inspiration based on traditional motifs

Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of retrieval results in the design inspiration drawing process of designers based on traditional motifs, a semantic data representation method of traditional motifs knowledge is put forward, and an effective knowledge base construction and knowledge retrieval method for traditional motifs are described. First, the connotation of traditional motifs is deeply explored based on the theory of symbol semantics, realizing the interpretation and transfer of the semantic features in traditional motifs. Second, shape grammar is adopted for fractal traditional motifs, the hierarchical structure of traditional motifs is built, and the parametric coding of semantic features of traditional motifs is completed, providing the semantic data representation method of traditional motifs knowledge. Third, based on the clustering algorithm, the construction method of the traditional motifs knowledge base is explained, and a traditional motifs knowledge retrieval method taking into account the user's perceptual preference is proposed. Finally, setting the traditional Chinese motifs as an example, with a tea set as the designed product, the construction process of a small traditional motif knowledge base is shown in detail, proving the feasibility of this paper’s theory. Compared with the traditional theory, the semantic data representation method of traditional pattern knowledge is greatly advantageous in semantic differences elimination and computational efficiency.     

A New Method for Reasoning about Action

Reasoning about action is an important aspect of common sense reasoning and planning.It gives rise to three classical problems:the frame problem,the qualification problem and the ramification problem.Existing approaches cannot deal with these problems efficiently.This paper presents a new method which uses the stratified ATMS for reasoning about action to overcome the limitations of these approaches.     

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.     

A causal approach to nonmonotonic reasoning

We introduce logical formalisms of production and causal inference relations based on input/output logics of Makinson and Van der Torre [J. Philos. Logic 29 (2000) 383–408]. These inference relations will be assigned, however, both standard semantics (giving interpretation to their rules), and natural nonmonotonic semantics based on the principle of explanation closure. The resulting nonmonotonic formalisms will be shown to provide a logical representation of abductive reasoning, and a complete characterization of causal nonmonotonic reasoning from McCain and Turner [Proc. AAAI-97, Providence, RI, 1997, pp. 460–465]. The results of the study suggest production and causal inference as general nonmonotonic formalisms providing an alternative representation for a significant part of nonmonotonic reasoning.     

A survey of modern knowledge modeling techniques

A major characteristic regarding developments in the broad field of artificial intelligence (AI) during the 1990s has been an increasing integration of AI with other disciplines. A number of other computer science fields and technologies have been used in developing intelligent systems, starting from traditional information systems and databases, to modern distributed systems and the Internet. This paper surveys the knowledge modeling techniques that have received most attention in recent years among developers of intelligent systems, AI practitioners and researchers. The techniques are described from two perspectives, theoretical and practical. Hence the first part of the paper presents major theoretical and architectural concepts, design approaches, and research issues. The second part deals with several practical systems, applications, and ongoing projects that use and implement the techniques described in the first part.     

A knowledge extraction and representation system for narrative analysis in the construction industry

Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.