scholarly journals Multicriterial Decision-Making in Multiagent Systems

2006 ◽  
pp. 711-718 ◽  
Author(s):  
Petr Tučník ◽  
Jan Kožaný ◽  
Vilém Srovnal
Keyword(s):  
Decision Making ◽  
Multiagent Systems

Decision Making Modeled as a Theorem Proving Process

2012 ◽  
Vol 4 (3) ◽  
pp. 1-11
Author(s):  
Jacques Calmet ◽  
Marvin Oliver Schneider
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information Technology ◽  
Multiagent Systems ◽  
Theorem Proving ◽  
Mathematical Knowledge ◽  
Knowledge Bases ◽  
Advanced Mathematics ◽  
Knowledge Communities ◽  
Computational Systems

The authors introduce a theoretical framework enabling to process decisions making along some of the lines and methodologies used to mechanize mathematics and more specifically to mechanize the proofs of theorems. An underlying goal of Decision Support Systems is to trust the decision that is designed. This is also the main goal of their framework. Indeed, the proof of a theorem is always trustworthy. By analogy, this implies that a decision validated through theorem proving methodologies brings trust. To reach such a goal the authors have to rely on a series of abstractions enabling to process all of the knowledge involved in decision making. They deal with an Agent Oriented Abstraction for Multiagent Systems, Object Mechanized Computational Systems, Abstraction Based Information Technology, Virtual Knowledge Communities, topological specification of knowledge bases using Logical Fibering. This approach considers some underlying hypothesis such that knowledge is at the heart of any decision making and that trust transcends the concept of belief. This introduces methodologies from Artificial Intelligence. Another overall goal is to build tools using advanced mathematics for users without specific mathematical knowledge.

scholarly journals Multiagent Decision Making and Learning in Urban Environments

2019 ◽  
Author(s):  
Akshat Kumar
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Strategies ◽  
Multiagent Systems ◽  
Intelligent Agents ◽  
Large Scale ◽  
Urban Environments ◽  
Aggregate Level ◽  
Domain Models ◽  
Self Driving Cars

Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents---from self-driving cars, ships to aerial drones---that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges---ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in large-scale urban multiagent systems.

scholarly journals The Interplay of Emotions and Norms in Multiagent Systems

2019 ◽  
Author(s):  
Anup K. Kalia ◽  
Nirav Ajmeri ◽  
Kevin S. Chan ◽  
Jin-Hee Cho ◽  
Sibel Adalı ◽  
...  
Keyword(s):  
Decision Making ◽  
Empirical Study ◽  
Multiagent Systems ◽  
Bayesian Models ◽  
Game Play ◽  
Empirically Supported ◽  
Log Likelihood ◽  
New Models ◽  
Better Than

We study how emotions influence norm outcomes in decision-making contexts. Following the literature, we provide baseline Dynamic Bayesian models to capture an agent's two perspectives on a directed norm. Unlike the literature, these models are holistic in that they incorporate not only norm outcomes and emotions but also trust and goals. We obtain data from an empirical study involving game play with respect to the above variables. We provide a step-wise process to discover two new Dynamic Bayesian models based on maximizing log-likelihood scores with respect to the data. We compare the new models with the baseline models to discover new insights into the relevant relationships. Our empirically supported models are thus holistic and characterize how emotions influence norm outcomes better than previous approaches.

Designing Agents with Negotiation Capabilities

2005 ◽  
pp. 810-815
Author(s):  
Jana Polgar
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Multiagent Systems ◽  
User Interfaces ◽  
Graphical User Interfaces ◽  
Decision Makers ◽  
The Internet ◽  
Decision Making Processes ◽  
Near Future

Agents are viewed as the next significant software abstraction, and it is expected they will become as ubiquitous as graphical user interfaces are today. Agents are specialized programs designed to provide services to their users. Multiagent systems have a key capability to reallocate tasks among the members, which may result in significant savings and improvements in many domains, such as resource allocation, scheduling, e-commerce, and so forth. In the near future, agents will roam the Internet, selling and buying information and services. These agents will evolve from their present day form - simple carriers of transactions - to efficient decision makers. It is envisaged that the decision-making processes and interactions between agents will be very fast (Kephart, 1998).

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