Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

Qualitative reasoning formalisms are an active research topic in artificial intelligence. In this survey we present a model-theoretic perspective on qualitative constraint reasoning and explain some of the basic concepts and results in an accessible way. In particular, we discuss the significance of omega-categoricity for qualitative reasoning, of primitive positive interpretations for complexity analysis, and of Datalog as a unifying language for describing local consistency algorithms.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

In this paper we discuss work performed in an ambitious DARPA funded cyber security effort. The broad approach taken by the project was for the network to be self-aware and to self-adapt in order to dodge attacks. In critical systems, it is not always the best or practical thing, to shut down the network under attack. The paper describes the qualitative trust modeling and diagnosis system that maintains a model of trust for networked resources using a combination of two basic ideas: Conditional trust (based on conditional preference (CP-Nets) and the principle of maximum entropy (PME)). We describe Monte-Carlo simulations of using adaptive security based on our trust model. The results of the simulations show the trade-off, under ideal conditions, between additional resource provisioning and attack mitigation.

The Qualitative Reasoning and Modelling (QRM) portal provides software tools (Garp3), documentation and support for users to build and simulate qualitative models. Qualitative Reasoning (QR) is an area of research within Artificial Intelligence (AI) that automates reasoning and problem solving about the (physical) world. It creates non-numerical descriptions of systems and their behaviour, preserving important behavioural properties and qualitative distinctions. Successful application areas include autonomous spacecraft support, failure analysis and on-board diagnosis of vehicle systems, automated generation of control software for photocopiers, conceptual knowledge capture in ecology, and intelligent aids for human learning (Bredeweg & Struss, 2003). Qualitative Reasoning has particularly value for developing, strengthening and further improving education and training on topics dealing with systems and their behaviour.

Chatter box abstraction eliminates chatter by performing a focused envisionment while Behavior Aggregation eliminations event occurrence branching. Describes a simulation technique that uses a cross between a state-based representation and a history-based representation. Models are decomposed into components and then each component is simulated separately. Temporal correlations between variables within different components is eliminated thus reducing many irrelevant distinctions within the behavioral description.

The 28th International Workshop on Qualitative Reasoning (QR-15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty.

When human experts build qualitative or quantitative models of complex systems, they use the function of the system as a guideline to decide what to model and how to model it, yet they do not often encode this functional knowledge directly. If qualitative and quantitative models contained this functional knowledge, our reasoning systems might use it as a heuristic or as a filter during the course of quantitative and qualitative simulation. Matthew Klenk (PARC) delivered a separate talk related to massive-scale model-based reasoning, describing the challenge of choosing initial conditions for simulation. Throughout the technical presentations on advances in qualitative simulation, we discussed the practicality of automatically transforming quantitative and qualitative models during the course of reasoning.

This paper tackles the problem of evaluating the degree of inconsistency in spatial and temporal qualitative reasoning. We first introduce postulates to propose a formal framework for measuring inconsistency in this context. Then, we provide two inconsistency measures that can be useful in various AI applications. The first one is based on the number of constraints that we need to relax to get a consistent qualitative constraint network. The second inconsistency measure is based on variable restrictions to restore consistency. It is defined from the minimum number of variables that we need to ignore to recover consistency. We show that our proposed measures satisfy required postulates and other appropriate properties. Finally, we discuss the impact of our inconsistency measures on belief merging in qualitative reasoning.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we take steps toward an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which examples of moral judgments and their justifications influence the moral development both of individuals and of the society as a whole. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.