Default reasoning using maximum entropy and variable strength defaults

PhDThe thesis presents a computational model for reasoning with partial information which uses default rules or information about what normally happens. The idea is to provide a means of filling the gaps in an incomplete world view with the most plausible assumptions while allowing for the retraction of conclusions should they subsequently turn out to be incorrect. The model can be used both to reason from a given knowledge base of default rules, and to aid in the construction of such knowledge bases by allowing their designer to compare the consequences of his design with his own default assumptions. The conclusions supported by the proposed model are justified by the use of a probabilistic semantics for default rules in conjunction with the application of a rational means of inference from incomplete knowledge the principle of maximum entropy (ME). The thesis develops both the theory and algorithms for the ME approach and argues that it should be considered as a general theory of default reasoning. The argument supporting the thesis has two main threads. Firstly, the ME approach is tested on the benchmark examples required of nonmonotonic behaviour, and it is found to handle them appropriately. Moreover, these patterns of commonsense reasoning emerge as consequences of the chosen semantics rather than being design features. It is argued that this makes the ME approach more objective, and its conclusions more justifiable, than other default systems. Secondly, the ME approach is compared with two existing systems: the lexicographic approach (LEX) and system Z+. It is shown that the former can be equated with ME under suitable conditions making it strictly less expressive, while the latter is too crude to perform the subtle resolution of default conflict which the ME approach allows. Finally, a program called DRS is described which implements all systems discussed in the thesis and provides a tool for testing their behaviours.Engineering and Physical Science Research Council (EPSRC

Dealing Automatically with Exceptions by Introducing Specificity in ASP

Answer Set Programming (ASP), via normal logic programs, is known as a suitable framework for default reasoning since it offers both a valid formal model and operational systems. However, in front of a real world knowledge representation problem, it is not easy to represent information in this framework. That is why the present article proposed to deal with this issue by generating in an automatic way the suitable normal logic program from a compact representation of the information. This is done by using a method, based on specificity, that has been developed for default logic and which is adapted here to ASP both in theoretical and practical points of view

A hybrid model for sharing information between fuzzy, uncertain and default reasoning models in multi-agent systems

This paper develops a hybrid model which provides a unified framework for the following four kinds of reasoning: 1)Zadeh's fuzzy approximate reasoning; 2) Truth qualification uncertain reasoning with respect to fuzzy propositions; 3)fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning); and 4)truth- qualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy; 2) uncertainty, linguistically expressed, can be handled in default reasoning; and 3) the four kinds of reasoning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogenous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledge-based multi-agent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper

An extension of the Eindhoven Classification Model to the educational sector

Series : Advances in intelligent systems research, ISSN 1951-685, vol. 33. "International Conference on Advanced Information and Communication Technology for Education (ICAICTE 2013) "This work presents an extension of the Eindhoven Classification Model to sort adverse events root causes for the Educational Sector. Extended Logic Programming was used for knowledge representation and reasoning with defective information, allowing for the modelling of the universe of discourse in terms of default data, information and knowledge. Indeed, a systematization of the evolution process of the body of knowledge in terms of Quality of Information (QoI) embedded in the Root Cause Analysis was accomplished, i.e., the knowledge representation and reasoning system proposed led to a process of QoI quantification that allowed the study of the event's root causes, on the fly.This work is funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational pro-gramme for competitiveness) and by Na-tional Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portu-guese Foundation for Science and Tech-nology) within project FCOMP-01-0124-FEDER-028980

Representing Representation : Integration between the Temporal Lobe and the Posterior Cingulate Influences the Content and Form of Spontaneous Thought

When not engaged in the moment, we often spontaneously represent people, places and events that are not present in the environment. Although this capacity has been linked to the default mode network (DMN), it remains unclear how interactions between the nodes of this network give rise to particular mental experiences during spontaneous thought. One hypothesis is that the core of the DMN integrates information from medial and lateral temporal lobe memory systems, which represent different aspects of knowledge. Individual differences in the connectivity between temporal lobe regions and the default mode network core would then predict differences in the content and form of people's spontaneous thoughts. This study tested this hypothesis by examining the relationship between seed-based functional connectivity and the contents of spontaneous thought recorded in a laboratory study several days later. Variations in connectivity from both medial and lateral temporal lobe regions was associated with different patterns of spontaneous thought and these effects converged on an overlapping region in the posterior cingulate cortex. We propose that the posterior core of the DMN acts as a representational hub that integrates information represented in medial and lateral temporal lobe and this process is important in determining the content and form of spontaneous thought

Combining Heuristics for Default Logic Reasoning Systems

In Artificial Intelligence, Default Logic is recognized as a powerful framework for knowledge representation when one has to deal with incomplete information. Its expressive power is suitable for nonmonotonic reasoning, but the counterpart is its very high level of theoretical complexity. Today, some operational systems are able to deal with real world applications. However finding a default logic extension in a practical way is not yet possible in whole generality. This paper shows how modern heuristics such as genetic algorithms and local search techniques can be used and combined to build an automated default reasoning system. We give a general description of the required basic components and we exhibit experimental result