13 results match your criteria Autonomous Agents And Multi-Agent Systems[Journal]

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Exploring the influence of a user-specific explainable virtual advisor on health behaviour change intentions.

Auton Agent Multi Agent Syst 2022 4;36(1):25. Epub 2022 Apr 4.

School of Mathematical and Physical Sciences, Macquarie University, Balaclava Road, Sydney, 2109 NSW Australia.

Virtual advisors (VAs) are being utilised almost in every service nowadays from entertainment to healthcare. To increase the user's trust in these VAs and encourage the users to follow their advice, they should have the capability of explaining their decisions, particularly, when the decision is vital such as health advice. However, the role of an explainable VA in health behaviour change is understudied. Read More

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Designing Empathic Virtual Agents: Manipulating Animation, Voice, Rendering, and Empathy to Create Persuasive Agents.

Auton Agent Multi Agent Syst 2022 Apr 22;36(1). Epub 2022 Feb 22.

Northeastern University, Boston, MA, USA.

Designers of virtual agents have a combinatorically large space of choices for the look and behavior of their characters. We conducted two between-subjects studies to explore the systematic manipulation of animation quality, speech quality, rendering style, and simulated empathy, and its impact on perceptions of virtual agents in terms of naturalness, engagement, trust, credibility, and persuasion within a health counseling domain. In the first study, animation was varied between manually created, procedural, or no animations; voice quality was varied between recorded audio and synthetic speech; and rendering style was varied between realistic and toon-shaded. Read More

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What values should an agent align with?: An empirical comparison of general and context-specific values.

Auton Agent Multi Agent Syst 2022 28;36(1):23. Epub 2022 Mar 28.

Delft University of Technology: Technische Universiteit Delft, Delft, The Netherlands.

The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general values (e.g. Read More

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Mandrake: multiagent systems as a basis for programming fault-tolerant decentralized applications.

Auton Agent Multi Agent Syst 2022 8;36(1):16. Epub 2022 Feb 8.

North Carolina State University, Raleigh, NC 27695 USA.

We conceptualize a software application as one constituted from agents that communicate via messaging. Modern software paradigms such as microservices and settings such as the Internet of Things evidence a growing interest in decentralized applications. Constructing a decentralized application involves designing agents as independent local computations that coordinate successfully to realize the application's requirements. Read More

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February 2022

Online revenue maximization for server pricing.

Auton Agent Multi Agent Syst 2022 22;36(1):11. Epub 2022 Jan 22.

University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA.

Efficient and truthful mechanisms to price resources on servers/machines have been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from the underlying distribution. Read More

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January 2022

Modelling and verification of reconfigurable multi-agent systems.

Auton Agent Multi Agent Syst 2021 26;35(2):47. Epub 2021 Aug 26.

University of Gothenburg, Gothenburg, Sweden.

We propose a formalism to model and reason about reconfigurable multi-agent systems. In our formalism, agents interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. Inspired by existing multi-robot systems, we represent a system as a set of agents (each with local state), executing independently and only influence each other by means of message exchange. Read More

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Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning.

Auton Agent Multi Agent Syst 2021 7;35(2):25. Epub 2021 Jun 7.

Department of Computer Science, University of Oxford, Oxford, UK.

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which they fail. In this work, we empirically investigate the learning power of various network architectures on a series of one-shot games. Read More

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Playing Atari with few neurons: Improving the efficacy of reinforcement learning by decoupling feature extraction and decision making.

Auton Agent Multi Agent Syst 2021 19;35(2):17. Epub 2021 Apr 19.

eXascale Infolab, Department of Computer Science, University of Fribourg, Fribourg, Switzerland.

We propose a new method for learning compact state representations and policies separately but simultaneously for policy approximation in vision-based applications such as Atari games. Approaches based on deep reinforcement learning typically map pixels directly to actions to enable end-to-end training. Internally, however, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it, two objectives which can be addressed independently. Read More

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Stable roommates with narcissistic, single-peaked, and single-crossing preferences.

Auton Agent Multi Agent Syst 2020 11;34(2):53. Epub 2020 Sep 11.

TU Berlin, Berlin, Germany.

The classical Stable Roommates problem is to decide whether there exists a matching of an even number of agents such that no two agents which are not matched to each other would prefer to be with each other rather than with their respectively assigned partners. We investigate Stable Roommates with complete (i.e. Read More

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September 2020

Toward Robust policy Summarization: Extended Abstract.

Auton Agent Multi Agent Syst 2019 May;2019:2081-2083

Technion - Israel Institute of Technology.

AI agents are being developed to help people with high stakes decision-making processes from driving cars to prescribing drugs. It is therefore becoming increasingly important to develop "explainable AI" methods that help people understand the behavior of such agents. Summaries of agent policies can help human users anticipate agent behavior and facilitate more effective collaboration. Read More

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Off-line synthesis of evolutionarily stable normative systems.

Auton Agent Multi Agent Syst 2018 2;32(5):635-671. Epub 2018 Jun 2.

3Department of Applied Mathematics and Computer Science, Universitat de Barcelona, Barcelona, Spain.

Within the area of multi-agent systems, normative systems are a widely used framework for the coordination of interdependent activities. A crucial problem associated with normative systems is that of synthesising norms that will effectively accomplish a coordination task and that the agents will comply with. Many works in the literature focus on the on-line synthesis of a single, (convention) whose compliance forms a rational choice for the agents and that effectively coordinates them in particular coordination situation that needs to be identified and modelled as a game in advance. Read More

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A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation.

Auton Agent Multi Agent Syst 2016 11 17;30(6):1148-1174. Epub 2016 Mar 17.

Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, VA 24061, USA, Tel.: +540-231-1248.

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Read More

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November 2016

Where to look? Automating attending behaviors of virtual human characters.

Auton Agent Multi Agent Syst 2001 Mar-Jun;4(1-2):9-23

Computer and Information Science Department, University of Pennsylvania, USA.

This research proposes a computational framework for generating visual attending behavior in an embodied simulated human agent. Such behaviors directly control eye and head motions, and guide other actions such as locomotion and reach. The implementation of these concepts, referred to as the AVA, draws on empirical and qualitative observations known from psychology, human factors and computer vision. Read More

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October 2002
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