A CASE-BASED MODEL OF CREATIVITY SCOT'[" R. TURNER Department of Computer Science University of California, Los Angeles Los Angeles CA 90024 USA Abstract Creativity - creating new solutions to problems - is an integral part of the problem-solving process. This paper presents a cognitive model of creativity in which a case-based problem-solver is augmented with (1) a creative drive and (2) a set of creativity heuristics. New solutions are discovered by solving a slightly different problem and adapting that solution to the original problem. By repeating this process, a creative problem-solver can discover new solutions that are novel, useful and very different from known solutions. This model has been implemented in a computer program called MINSTREL. MINSTREL has been used for planning and problem-solving, to tell stories, and to invent mechanical devices. 1 Introduction Creativity is an important element of human cognition. We all invent on a daily basis: we fix cars using spare change and bailing wire, invent jokes based on the latest domestic crisis, and make up bedtime stories for our children. The ability to invent original, useful solutions to problems is a fundamental process of human thought. To understand human cognition, we must understand the processes of creativity: the goals that drive people to create and the mechanisms they use to invent novel and useful solutions to their problems. This paper presents a model of creative reasoning as an extension to case-based problem-solving.
This paper reports on the findings of an ongoing project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers which experience abrupt, partial or full failure of component devices. The problem we address is: given a hybrid model of system behavior, a history of executed controller actions, and a history of observations, including an observation of behavior that is aberrant relative to the model of expected behavior, determine what fault occurred to have caused the aberrant behavior. Determining a diagnosis can be cast as a search problem to find the most likely model for the data. Unfortunately, the search space is extremely large. To reduce search space size and to identify an initial set of candidate diagnoses, we propose to exploit techniques originally applied to qualitative diagnosis of continuous systems. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.
The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with constraint networks, defining the semantics and graphical representation. We also introduce the AND/OR search space for graphical models, and develop a new linear space search algorithm. This provides the basis for understanding the benefits of processing the constraint information separately, resulting in the pruning of the search space. When the constraint part is tractable or has a small number of solutions, using the mixed representation can be exponentially more effective than using pure belief networks which odel constraints as conditional probability tables.
Sustainability requires emphasizing the importance of environmental causes and effects among design knowledge from heterogeneous stakeholders to make a sustainable decision. Recently, such causes and effects have been well developed in ontological representation, which has been challenged to generate and integrate multiple domain knowledge due to its domain specific characteristics. Moreover, it is too challengeable to represent heterogeneous, domain-specific design knowledge in a standardized way. Causal knowledge can meet the necessity of knowledge integration in domains. Therefore, this paper aims to develop a causal knowledge integration system with the authors’ previous mathematical causal knowledge representation.
Model counting and weighted model counting are key problems in artificial intelligence. Marginal inference can be reduced to model counting in many statistical-relational systems, such as Markov Logic. One common approach used by model counters is splitting a theory into disjoint subtheories, performing model counting on the subtheories, and then caching the result. If an identical subtheory is encountered again in the search, the cached result is used, greatly reducing runtime. In this work we introduce a way to cache symmetric subtheories compactly, which could potentially decrease required cache size, increase cache hits, and decrease runtime of solving.