The ability of an agent to distinguish the ramification effects of an action from its direct effects adds value to the explainability of its decisions. In this work, we propose to encode the ramification effects of ontic and epistemic actions as single-point update models in an epistemic planning domain modeled with Kripkean semantics of Knowledge and Belief. We call them “mental actions”. We discuss a preliminary approach to realize our idea, and we conclude by pointing out some optimizations as our ongoing pursuit.

Regular expressions are an important building block of rule-based information extraction systems. Regexes can encode rules to recognize instances of simple entities which can then feed into the identification of more complex cross-entity relationships. Manually crafting a regex that recognizes all possible instances of an entity is difficult since an entity can manifest in a variety of different forms. Thus, the problem of automatically generalizing manually crafted seed regexes to improve the recall of IE systems has attracted research attention. In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex. Our approach starts from a manually authored high precision seed regex for the entity of interest, and uses the matches of the seed regex and the context around these matches to identify more instances of the entity. These are then used to identify a set of diverse, high recall regexes that are representative of this entity. Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach.

For over twenty five years Ford has been utilizing an AI-based system to manage process planning for vehicle assembly at our assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford’s manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to re-engineer and convert the KL-ONE ontology into a semantic web OWL/RDF format. In this paper, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed Semantic Web technology in our application.

In this paper, we look into the problem of filtering problem solution repositories (from sources such as community-driven question answering systems) to render them more suitable for usage in knowledge reuse systems. We explore harnessing the fuzzy nature of usability of a solution to a problem, for such compaction. Fuzzy usabilities lead to several challenges; notably, the trade-off between choosing generic or better solutions. We develop an approach that can heed to a user specification of the trade-off between these criteria and introduce several quality measures based on fuzzy usability estimates to ascertain the quality of a problem-solution repository for usage in a Case Based Reasoning system. We establish, through a detailed empirical analysis, that our approach outperforms state-of-the-art approaches on virtually all quality measures.

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop a clustering algorithm toward the outlined goal of building interpretable and reconfigurable cluster models; it works by generating rules with disjunctions and conditions on the frequencies of words, to decide on the membership of a document to a cluster. Each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. We show that our approach outperforms the unsupervised decision tree approach by huge margins. We show that the purity and f-measure losses to achieve interpretability are as little as 5% and 3% respectively using our approach.