Autonomous Hypothesis Generation for Knowledge Discovery in Continuous Domains

Advances of computational power, data collection and storage techniques are making new data available every day. This situation has given rise to hypothesis generation research, which complements conventional hypothesis testing research. Hypothesis generation research adopts techniques from machine learning and data mining to autonomously uncover causal relations among variables in the form of previously unknown hidden patterns and models from data. Those patterns and models can come in different forms (e.g. rules, classifiers, clusters, causal relations). In some situations, data are collected without prior supposition or imposition of a specific research goal or hypothesis. Sometimes domain knowledge for this type of problem is also limited. For example, in sensor networks, sensors constantly record data. In these data, not all forms of relationships can be described in advance. Moreover, the environment may change without prior knowledge. In a situation like this one, hypothesis generation techniques can potentially provide a paradigm to gain new insights about the data and the underlying system. This thesis proposes a general hypothesis generation framework, whereby assumptions about the observational data and the system are not predefined. The problem is decomposed into two interrelated sub-problems: (1) the associative hypothesis generation problem and (2) the causal hypothesis generation problem. The former defines a task of finding evidence of the potential causal relations in data. The latter defines a refined task of identifying casual relations. A novel association rule algorithm for continuous domains, called functional association rule mining, is proposed to address the first problem. An agent based causal search algorithm is then designed for the second problem. It systematically tests the potential causal relations by querying the system to generate specific data; thus allowing for causality to be asserted. Empirical experiments show that the functional association rule mining algorithm can uncover associative relations from data. If the underlying relationships in the data overlap, the algorithm decomposes these relationships into their constituent non-overlapping parts. Experiments with the causal search algorithm show a relative low error rate on the retrieved hidden causal structures. In summary, the contributions of this thesis are: (1) a general framework for hypothesis generation in continuous domains, which relaxes a number of conditions assumed in existing automatic causal modelling algorithms and defines a more general hypothesis generation problem; (2) a new functional association rule mining algorithm, which serves as a probing step to identify associative relations in a given dataset and provides a novel functional association rule definition and algorithms to the literature of association rule mining; (3) a new causal search algorithm, which identifies the hidden causal relations of an unknown system on the basis of functional association rule mining and relaxes a number of assumptions commonly used in automatic causal modelling

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

Design thinking support: information systems versus reasoning

Numerous attempts have been made to conceive and implement appropriate information systems to support architectural designers in their creative design thinking processes. These information systems aim at providing support in very diverse ways: enabling designers to make diverse kinds of visual representations of a design, enabling them to make complex calculations and simulations which take into account numerous relevant parameters in the design context, providing them with loads of information and knowledge from all over the world, and so forth. Notwithstanding the continued efforts to develop these information systems, they still fail to provide essential support in the core creative activities of architectural designers. In order to understand why an appropriately effective support from information systems is so hard to realize, we started to look into the nature of design thinking and on how reasoning processes are at play in this design thinking. This investigation suggests that creative designing rests on a cyclic combination of abductive, deductive and inductive reasoning processes. Because traditional information systems typically target only one of these reasoning processes at a time, this could explain the limited applicability and usefulness of these systems. As research in information technology is increasingly targeting the combination of these reasoning modes, improvements may be within reach for design thinking support by information systems

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers