agent communication
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2022 ◽  
Author(s):  
Juliao Braga ◽  
Joao Nuno Silva ◽  
Patricia Takako Endo ◽  
Jessica Ribas ◽  
Nizam Omar

This paper describes the development and implementation of a blockchain to improve security, knowledge and intel ligence during the communication and col laboration processes between agents under restricted Internet Infrastructure domains. It is a work that proposes the application of a blockchain, independent of platform, in a particular model of agents, but that can be used in similar proposals, since the results in the specific model were satisfactory. Additional ly, the model al lows interaction and, also, col laboration between humans and agents.


2022 ◽  
Author(s):  
Titas Bera ◽  
Mohit Ludhiyani ◽  
Arup K. Sadhu ◽  
Ranjan Dasgupta

2021 ◽  
pp. 1-39
Author(s):  
Alison R. Panisson ◽  
Peter McBurney ◽  
Rafael H. Bordini

There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in the literature. Such benefits come not only from the expressiveness that argumentation-based techniques bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation enables for autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes (reasoning patterns for argumentation) are useful in addressing the requirements of the application domain in regards to argumentation (e.g., defining the scope in which argumentation will be used by agents in that particular application). In this work, we propose an argumentation framework that takes into account the particular structure of argumentation schemes at its core. This paper formally defines such a framework and experimentally evaluates its implementation for both argumentation-based reasoning and dialogues.


2021 ◽  
Author(s):  
Razvan-Adrian Luchian ◽  
Sabin Rosioru ◽  
Iulia Stamatescu ◽  
Ioana Fagarasan ◽  
Grigore Stamatescu

2021 ◽  
Author(s):  
Aaron Young ◽  
Jay Taves ◽  
Asher Elmquist ◽  
Radu Serban ◽  
Dan Negrut ◽  
...  

Abstract We describe a simulation environment that enables the development and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the design and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies are learned on rigid terrain and are subsequently shown to transfer successfully to hard (silt-like) and soft (snow-like) deformable terrains. The enabling simulation environment is developed from the high fidelity, physics-based simulation engine Chrono. Five Chrono modules are employed herein: Chrono::Engine, Chrono::Vehicle, PyChrono, SynChrono and Chrono::Sensor. Vehicle’s are modeled using Chrono::Engine and Chrono::Vehicle and deployed on deformable terrain within the training/testing environment. Utilizing the Python interface to the C++ Chrono API called PyChrono and OpenAI Gym’s supporting infrastructure, training is conducted in a GymChrono learning environment. The GymChrono-generated policy is subsequently deployed for testing in SynChrono, a scalable, cluster-deployable multi-agent testing infrastructure built on MPI. SynChrono facilitates inter-agent communication and maintains time and space coherence between agents. A sensor modeling tool, Chrono::Sensor, supplies sensing data that is used to inform agents during the learning and inference processes. The software stack and the Chrono simulator are both open source. Relevant movies: [1].


2021 ◽  
Vol 12 ◽  
Author(s):  
Stefan Kopp ◽  
Nicole Krämer

The study of human-human communication and the development of computational models for human-agent communication have diverged significantly throughout the last decade. Yet, despite frequently made claims of “super-human performance” in, e.g., speech recognition or image processing, so far, no system is able to lead a half-decent coherent conversation with a human. In this paper, we argue that we must start to re-consider the hallmarks of cooperative communication and the core capabilities that we have developed for it, and which conversational agents need to be equipped with: incremental joint co-construction and mentalizing. We base our argument on a vast body of work on human-human communication and its psychological processes that we reason to be relevant and necessary to take into account when modeling human-agent communication. We contrast those with current conceptualizations of human-agent interaction and formulate suggestions for the development of future systems.


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