Non-annual queries and events

This disclosure describes techniques that provide a user with information relating to non-annual events. For example, such events include a 10,000-day birthday, billionth second since wedding, etc. With user permission, a platform such as a social media service, a virtual assistant, etc. accesses information such as personal profile, contact information, online calendars, etc. to determine non-annual events likely of interest to a user, and to formulate answers to time-seeking queries requested by the user in non-annual units. An option is provided to the user to share milestones expressed in non-annual time units

The UMASS intelligent home project.

Abstract Intelligent environments are an interesting development and research application problem for multi-agent systems. The functional and spatial distribution of tasks naturally lends itself to a multi-agent model and the existence of shared resources creates interactions over which the agents must coordinate. In the UMASS Intelligent Home project we have designed and implemented a set of distributed autonomous home control agents and deployed them in a simulated home environment. Our focus is primarily on resource coordination, though this project has multiple goals and areas of exploration ranging from the intellectual evaluation of the application as a general MAS testbed to the practical evaluation of our agent building and simulation tools

Quantitative organizational modeling and design for multi-agent systems

As the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants\u27 interactions. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents particular and different roles, responsibilities and peers. These additional constraints can allow agents to operate effectively within a large-scale system, with little or no sacrifice in utility. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand and model the behavior of candidate designs. In the, multi-agent systems community, relatively little attention has been paid to understanding and comparing organizations at a quantitative level. In this thesis, I show that it is possible to develop such an understanding, and in particular I show how quantitative information can form the basis of a predictive, proscriptive organizational model. This can in turn lead to more efficient, robust and context-sensitive systems by increasing the level of detail at which competing organizational designs are evaluated. To accomplish this, I introduce a new, domain-independent organizational design representation able to model and predict the quantitative performance characteristics of agent organizations. This representation, capable of capturing a wide range of multi-agent characteristics in a single, succinct model, supports the selection of an appropriate design given a particular operational context. I demonstrate the representational capabilities and efficacy of the language by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. In addition to their predictive ability, these same models also describe the range of possible organizations in those domains. I show how general search techniques can be used to explore this space, using those quantitative predictions to evaluate alternatives and enable automated organizational design

Using Self-Diagnosis to Adapt Organizational Structures

The specific organization used by a multi-agent system is crucial for its effectiveness and efficiency. In dynamic environments, or when the objectives of the system shift, the organization must therefore be able to change as well. In this abstract we propose using a general diagnosis engine to drive this process of adaptation, using the TÆMS modeling language as the primary representation of organizational information

Using Diagnosis to Learn Contextual Coordination Rules

Knowing when and how to communicate and coordinate with other agents in a multi-agent system is an important efficiency and reliability question. Contextual rules governing this communication must be provided to the agent, or generated at runtime through environmental analysis. In this paper we describe how the TAEMS task modeling language is used to encode such contextual coordination rules, and how runtime diagnosis can be used to dynamically update them. Overview Communication and coordination is an essential component of most complex multi-agent systems. Contention over shared resources, the desire to employ remote information and the need to coordinate interrelated activities may each require some sort of information transfer between agents to be resolved. To this end, individual actors in a multi-agent system must be able to explicitly or implicitly communicate requests and results, desires and beliefs, to make the system an efficient and cohesive unit. Thus, a set of situation-..