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

Automated travel planning

This paper summarizes the current state of art in the domain of automated travel planning. Requirements for planning systems are identified taking into account both functionality and personalization aspects of such systems. A new algorithm that allows planning routes between any two locations and that utilizes combination of various means of transportation is discussed

Leadership After COVID-19 [Spoiler: There is Something Beyond Recovery and Resilience for Individuals and Organizations]

Throughout history, humans have found ways to recycle energy in ways that benefit them and would otherwise be wasted. Examples of this processing include learning to use fire for warmth, light, and cooking or identifying how to redirect the wind to navigate a ship in a preferred direction as opposed to a undetermined random route. Today, leaders can learn how to harvest uncertainty and randomness into tasks that utilize their strengths, as well as those on the team, to fuel optimal business outcomes and well-being for their employees. The author provides two workshop outlines. The first will help leaders correct cognitive distortions and view an uncertain world more objectively. The second workshop will use a validated framework to reframe uncertainty inherent in work tasks as opportunities to utilize strengths that to energize individuals and maximize resources within any organization

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Transistor scaled HPC application performance

We propose a radically new, biologically inspired, model of extreme scale computer on which ap- plication performance automatically scales with the transistor count even in the face of component failures. Today high performance computers are massively parallel systems composed of potentially hundreds of thousands of traditional processor cores, formed from trillions of transistors, consuming megawatts of power. Unfortunately, increasing the number of cores in a system, unlike increasing clock frequencies, does not automatically translate to application level improvements. No general auto-parallelization techniques or tools exist for HPC systems. To obtain application improvements, HPC application programmers must manually cope with the challenge of multicore programming and the significant drop in reliability associated with the sheer number of transistors. Drawing on biological inspiration, the basic premise behind this work is that computation can be dramatically accelerated by integrating a very large-scale, system-wide, predictive associative memory into the operation of the computer. The memory effectively turns computation into a form of pattern recognition and prediction whose result can be used to avoid significant fractions of computation. To be effective the expectation is that the memory will require billions of concurrent devices akin to biological cortical systems, where each device implements a small amount of storage, computation and localized communication. As typified by the recent announcement of the Lyric GP5 Probability Processor, very efficient scalable hardware for pattern recognition and prediction are on the horizon. One class of such devices, called neuromorphic, was pioneered by Carver Mead in the 80’s to provide a path for breaking the power, scaling, and reliability barriers associated with standard digital VLSI tech- nology. Recent neuromorphic research examples include work at Stanford, MIT, and the DARPA Sponsored SyNAPSE Project. These devices operate transistors as unclocked analog devices orga- nized to implement pattern recognition and prediction several orders of magnitude more efficiently than functionally equivalent digital counterparts. Abstractly, the devices can be used to implement modern machine learning or statistical inference. When exposed to data as a time-varying signal, the devices learn and store patterns in the data at multiple time scales and constantly provide predictions about what the signal will do in the future. This kind of function can be seen as a form of predictive associative memory. In this paper we describe our model and initial plans for exploring it.Department of Energy Office of Science (DE-SC0005365), National Science Foundation (1012798

Impulsive Compulsive Behaviours in Older Adults: Rethinking Our Approach To Predictors, Breadth and Assessment

openIndividuals with Impulsive Compulsive Behaviours (ICBs) experience difficulties in resisting an urge to engage in a reward-based action, resulting in problematic excessive engagement in the behaviour. While seven subtypes of ICBs have been commonly researched as non-motor symptoms in Parkinson’s disease (ICD), the breadth of ICBs, the harm related to them, and the risk factors involved in the development and maintenance of heterogeneous expressions of ICBs have been overlooked in the general population. This cross-sectional study explores ICBs among the general population, highlighting their prevalence in non-clinical populations and proposing a framework for future studies among clinical populations. A sample of 71 older adults from the UK completed a survey comprising seven adapted self-report questionnaires that were proposed as reflective of components of the first model on the addictive cycle of ICBs (I-PACE). Qualitative analyses revealed a variety of behaviours considered problematic among older adults, suggesting that ICBs reflect phenotypical expressions of difficulties with impulse-control, obsessive-compulsivity and substance-use. Correlations between outcome measures of ICBs revealed a strong association between the severity of symptoms and ICB-related harm (i.e., financial, social, health). Principal Component Analyses reduced the dimensionality, while linear regression analyses and between-group ANOVAs explored the key components contributing to ICBs and their subtypes and the main predictors of the ICB-Checklist, SGHS-18 Harm Screen and QUIP-rs. Assessment of ICBs needs to be sensitive to both problematic impulses and compulsions, while their consequences on well-being need to be viewed from a medical and biopsychosocial perspective. Future studies should further explore the risks of obsessions, compulsions and the motivation for ICBs.Individuals with Impulsive Compulsive Behaviours (ICBs) experience difficulties in resisting an urge to engage in a reward-based action, resulting in problematic excessive engagement in the behaviour. While seven subtypes of ICBs have been commonly researched as non-motor symptoms in Parkinson’s disease (ICD), the breadth of ICBs, the harm related to them, and the risk factors involved in the development and maintenance of heterogeneous expressions of ICBs have been overlooked in the general population. This cross-sectional study explores ICBs among the general population, highlighting their prevalence in non-clinical populations and proposing a framework for future studies among clinical populations. A sample of 71 older adults from the UK completed a survey comprising seven adapted self-report questionnaires that were proposed as reflective of components of the first model on the addictive cycle of ICBs (I-PACE). Qualitative analyses revealed a variety of behaviours considered problematic among older adults, suggesting that ICBs reflect phenotypical expressions of difficulties with impulse-control, obsessive-compulsivity and substance-use. Correlations between outcome measures of ICBs revealed a strong association between the severity of symptoms and ICB-related harm (i.e., financial, social, health). Principal Component Analyses reduced the dimensionality, while linear regression analyses and between-group ANOVAs explored the key components contributing to ICBs and their subtypes and the main predictors of the ICB-Checklist, SGHS-18 Harm Screen and QUIP-rs. Assessment of ICBs needs to be sensitive to both problematic impulses and compulsions, while their consequences on well-being need to be viewed from a medical and biopsychosocial perspective. Future studies should further explore the risks of obsessions, compulsions and the motivation for ICBs