Asimovian Adaptive Agents

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results

Restructuring Health Insurance Markets

Examines six possible structural changes to the health insurance market to expand coverage, including rate compression, high-risk pools, and an insurance exchange. Outlines their benefits and the most effective way to structure and implement them

SAsSy – Scrutable Autonomous Systems

Abstract. An autonomous system consists of physical or virtual systems that can perform tasks without continuous human guidance. Autonomous systems are becoming increasingly ubiquitous, ranging from unmanned vehicles, to robotic surgery devices, to virtual agents which collate and process information on the internet. Existing autonomous systems are opaque, limiting their usefulness in many situations. In order to realise their promise, techniques for making such autonomous systems scrutable are therefore required. We believe that the creation of such scrutable autonomous systems rests on four foundations, namely an appropriate planning representation; the use of a human understandable reasoning mechanism, such as argumentation theory; appropriate natural language generation tools to translate logical statements into natural ones; and information presentation techniques to enable the user to cope with the deluge of information that autonomous systems can provide. Each of these foundations has its own unique challenges, as does the integration of all of these into a single system.

Overview of Final Exchange Regulations

Summarizes rules and regulations for establishing and operating state health insurance exchanges, as well as implications for eligibility determinations, verification rules, qualified health plans, and user fees and financial support for exchanges

Towards Verifiably Ethical Robot Behaviour

Ensuring that autonomous systems work ethically is both complex and difficult. However, the idea of having an additional `governor' that assesses options the system has, and prunes them to select the most ethical choices is well understood. Recent work has produced such a governor consisting of a `consequence engine' that assesses the likely future outcomes of actions then applies a Safety/Ethical logic to select actions. Although this is appealing, it is impossible to be certain that the most ethical options are actually taken. In this paper we extend and apply a well-known agent verification approach to our consequence engine, allowing us to verify the correctness of its ethical decision-making.Comment: Presented at the 1st International Workshop on AI and Ethics, Sunday 25th January 2015, Hill Country A, Hyatt Regency Austin. Will appear in the workshop proceedings published by AAA

Architecture for spacecraft operations planning

A system which generates plans for the dynamic environment of space operations is discussed. This system synthesizes plans by combining known operations under a set of physical, functional, and temperal constraints from various plan entities, which are modeled independently but combine in a flexible manner to suit dynamic planning needs. This independence allows the generation of a single plan source which can be compiled and applied to a variety of agents. The architecture blends elements of temperal logic, nonlinear planning, and object oriented constraint modeling to achieve its flexibility. This system was applied to the domain of the Intravehicular Activity (IVA) maintenance and repair aboard Space Station Freedom testbed

Cooperating Agents for 3D Scientific Data Interpretation

Many organizations collect vast quantities of three-dimensional (3-D) scientific data in volumetric form for a range of purposes, including resource exploration, market forecasting, and process modelling. Traditionally, these data have been interpreted by human experts with only minimal software assistance. However, such manual interpretation is a painstakingly slow and tedious process. Moreover, since interpretation involves subjective judgements and each interpreter has different scientific knowledge and experience, formulation of an effective interpretation often requires the cooperation of numerous such experts. Hence, there is a pressing need for a software system in which individual interpretations can be generated automatically and then refined through the use of cooperative reasoning and information sharing. To this end, a prototype system, SurfaceMapper, has been developed in which a community of cooperating software agents automatically locate and display interpretations in a volume of 3-D scientific data. The challenges and experiences in designing and building such a system are discussed. Particular emphasis is given to the agents' interactions and an empirical evaluation of the effectiveness of different cooperation strategies is presented

The Obama Administration's 2010 Call Letter for Medicare Advantage and Prescription Drug Plans: Implications for Beneficiaries

Outlines key provisions and changes in the Medicare Advantage or Medicare Prescription Drug plans in 2010. Discusses requirements to improve accountability, promote informed health plan choices, and increase beneficiary protections

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine