KR3^3: An Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge. An action language is used for the low-level (LL) and high-level (HL) system descriptions in the architecture, and the definition of recorded histories in the HL is expanded to allow prioritized defaults. For any given goal, tentative plans created in the HL using default knowledge and commonsense reasoning are implemented in the LL using probabilistic algorithms, with the corresponding observations used to update the HL history. Tight coupling between the two levels enables automatic selection of relevant variables and generation of suitable action policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in large and complex domains. The architecture is evaluated in simulation and on physical robots transporting objects in indoor domains; the benefit on robots is a reduction in task execution time of 39% compared with a purely probabilistic, but still hierarchical, approach.Comment: The paper appears in the Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014

Bridging the Gap between Flexibility and Rigidity: Unifying Relationships with a Hybrid Link Architecture

Current knowledge systems lack an effective architecture to bridge the well-acknowledged gap between flexibility and rigidity - users’ preference of flexible expression and machine’s need for rigorous representation. This paper focuses on finding an adequate set of relationship types (or link types) and map flexible expressions from users to link types in propositional knowledge systems, in which knowledge elements are stored as “concept-relationship-concept” triplets. We call our approach “hybrid link architecture”. This architecture has two levels of presentations, an open link name layer and a closed link type layer. Between these two layers is a matching mechanism based on a psycholinguistic thesaurus to map names to types. The link types are derived by synthesizing literature of knowledge organization, semantic network, and educational taxonomies. A two-stage evaluation is conducted based on 39,706 triplets. The evaluation shows that the matching mechanism is accurate and the proposed link types are mutually exclusive and cover most link names in our dataset

An Analysis of Risk-Taking Behavior for Public Defined Benefit Pension Plans

The current pension funding gap for public employees through state-sponsored defined benefit (DB) plans causes concern. The effect of underfunded pension plans and the consequent risk-taking touch a large percentage of the population, including those at risk and the taxpayers who may be ultimately called upon to close the funding gap The severe funding gap prompts questions of why the underfunding occurred, and how the underfunding will affect state and local budgets, public employee retirement benefits, and employment security. A pertinent question is whether pension fund administrators will adopt riskier investment strategies in the hope of raising pension fund returns and lowering shortfalls, thus minimizing state pension contributions and reducing state budget gaps. In this research project, the authors focus on risk-taking behavior of public pension plans. In particular, the first comprehensive analysis of the determinants of public pension risk-taking behavior during the period 2001 through 2009 will be provided. Researchers will also investigate whether states reduced pension investment risk after the recent financial crisis. These results will have important policy relevance regarding public employee retirement benefit and job security, tax policies, state fiscal constraints, political influence, and public employee unionization, among other issues