2,626 research outputs found
Towards a goal-oriented agent-based simulation framework for high-performance computing
Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft
Anytime planning for agent behaviour
For an agent to act successfully in a complex and dynamic environment (such as a computer game)it must have a method of generating future behaviour that meets the demands of its environment. One such method is anytime planning. This paper discusses the problems and benefits associated with making a planning system work under the anytime paradigm, and introduces Anytime-UMCP (A-UMCP), an anytime version of the UMCP hierarchical task network (HTN) planner [Erol, 1995]. It also covers the necessary abilities an agent must have in order to execute plans produced by an anytime hierarchical task network planner
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
Motivational Interviewing Impact on Cardiovascular Disease
abstract: Harm reduction in cardiovascular disease is a significant problem worldwide. Providers, families, and healthcare agencies are feeling the burdens imparted by these diseases. Not to mention missed days of work and caregiver strain, the losses are insurmountable. Motivational interviewing (MI) is gaining momentum as a method of stimulating change through intrinsic motivation by resolving ambivalence toward change (Ma, Zhou, Zhou, & Huang, 2014). If practitioners can find methods of educating the public in a culturally-appropriate and sensitive manner, and if they can work with community stakeholders to organize our resources to make them more accessible to the people, we may find that simple lifestyle changes can lead to risk reduction of cardiovascular diseases. By working with our community leaders and identifying barriers unique to each population, we can make positive impacts on a wide range of issues that markedly impact our healthcare systems
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An Ontological formalization of the planning task
In this paper we propose a generic task ontology, which formalizes the space of planning problems. Although planning is one of the oldest researched areas in Artificial Intelligence and attempts have been made in the past at developing task ontologies for planning, these formalizations suffer from serious limitations: they do not exhibit the required level of formalization and precision and they usually fail to include some of the key concepts required for specifying planning problems. In con-trast with earlier proposals, our task ontology formalizes the nature of the planning task independently of any planning paradigm, specific domains, or applications and provides a fine-grained, precise and comprehensive characterization of the space of planning problems. Finally, in addition to producing a formal specification we have also operationalized the ontology into a set of executable definitions, which provide a concrete reusable resource for knowledge acquisition and system development in planning applications
Personal and household income taxation in a progressive tax system: evidence from Italy.
I compare personal and household income taxation and study the effects of tax progression under the two systems. Potential reforms of the Italian tax system are simulated, endogenizing labor supply reactions. Results show that, with respect to a number of indicators, the choice of the tax unit is more relevant than the degree of progression of the tax schedule. A personal and progressive tax system provides incentives to female labor supply and turns out to be the most effective in redistributing income and raising revenue, with little productive costs compared with a flat tax rate. Household taxation has instead a number of drawbacks when coupled with a progressive tax schedule.
PERSONAL AND HOUSEHOLD INCOME TAXATION IN A PROGRESSIVE TAX SYSTEM: EVIDENCE FROM ITALY
I compare personal and household income taxation and study the effects of tax progression under the two systems. Potential reforms of the Italian tax system are simulated, endogenizing labor supply reactions. Results show that, with respect to a number of indicators, the choice of the tax unit is more relevant than the degree of progression of the tax schedule. A personal and progressive tax system provides incentives to female labor supply and turns out to be the most effective in redistributing income and raising revenue, with little productive costs compared with a flat tax rate. Household taxation has instead a number of drawbacks when coupled with a progressive tax schedule.
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