Abstract: Causal reasoning perceptions play an essential role in human decision-making. Recognizing and developing causal relationships is essential for reasoning; it forms the basis for acting intelligentially in the world. Causal knowledge provides a deep understanding of a system; and, the potential control over a system that comes from being able to predict action's consequences. Relationships with a known cause/effect relationship have a high decision value. Causality description must necessarily be imperfect as knowledge is imperfect and limited. Commonsense understanding of the world tells us that we have to deal with imprecision, uncertainty and imperfect knowledge. Consequently, knowledge of at least some causal effects is inherently imprecise. A difficulty is striking a good balance between precise formalism and commonsense imprecise reality.
Causality is imprecisely granular in many ways. Causal complexes are groupings of smaller causal relations that make up a large grained causal object. Usually, commonsense reasoning is more successful in reasoning about a few large-grained events than many fine-grained events. However, the larger-grained causal objects are necessarily more imprecise as some of their constituent components. A satisficing solution might be to develop large-grained solutions and then only go to the finer-grain when the impreciseness of the large-grain is unsatisfactory.

Brief Biography of the Speaker:
Professor Mazlack studied computer science and applied mathematics at Washington University (St. Louis) and electrical engineering at both SDSM&T and Marquette University. He received his Doctorate of Science from Washington University. He also studied philosophy at both Washington University and at Marquette University. Along the way to his degrees, he did research in computer science, electrical engineering, and biomedical engineering. At Marquette both a Bacon Scholarship and an athletic scholarship (football) supported him. He is a member of the Omega Rho honorary. He has been a visiting scholar at the University of California, Berkeley (imprecise reasoning) and at the University of Geneva (computational linguistics). He is on the editorial board of several journals and has served on the program committee of many conferences.
Dr. Mazlack currently is at the University of Cincinnati where he is the head of the Applied Artificial Intelligence Laboratory and the chair of the Data and Knowledge Management research group. Beyond academia, at a large computer company, he was responsible for database software development. He has been closely involved with several small company startups. Away from technology, he has been professionally active in the visual, written, and dramatic arts.
Dr. Mazlack's current research is directed toward three areas:
• Causality, both theoretical and applied to observational data.
• Unsupervised data mining and the closely associated topic of autonomous recognition of web page ontologies in the context of the Semantic Web.
• Clustering multi-modal computational objects.
These interests are in the context of broader interests in: soft computing, natural language understanding, artificial intelligence, and databases.