Human-Agent Decision-making: Combining Theory and Practice

Extensive work has been conducted both in game theory and logic to model strategic interaction. An important question is whether we can use these theories to design agents for interacting with people? On the one hand, they provide a formal design specification for agent strategies. On the other hand, people do not necessarily adhere to playing in accordance with these strategies, and their behavior is affected by a multitude of social and psychological factors. In this paper we will consider the question of whether strategies implied by theories of strategic behavior can be used by automated agents that interact proficiently with people. We will focus on automated agents that we built that need to interact with people in two negotiation settings: bargaining and deliberation. For bargaining we will study game-theory based equilibrium agents and for argumentation we will discuss logic-based argumentation theory. We will also consider security games and persuasion games and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729

Decision Making Intelligent Agent on SOX Compliance over the Imports Process

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Imports Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Imports Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence

Decision Making Intelligent Agent on SOX Compliance over the Goods Receipt Processs

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Goods Receipt Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Goods Receipt Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence

An opinion diffusion model with deliberation

In this article, we propose an agent-based model of opinion diffusion and voting where agents influence each other through deliberation. The model is inspired from social modeling as it describes a process of collective decision-making that iterates on a series of dyadic inter-individual influence steps and collective deliberation procedures. We study the evolution of opinions and the correctness of decisions taken within a group. We also aim at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and agent-based influence models, which are not obvious to link since a formal translation and interpretation of their relationship is required. From a sequence of controlled simulations, we find that deliberation, modeled as an exchange of arguments, reduces the variance of opinions and the number of extremists, as long as not too much deliberation takes place during the decision-making process. Insofar as we define “correct” decisions as those whose supporting arguments survive deliberation, promoting deliberative discussion favors convergence towards correct decisions

Tasks for Agent-Based Negotiation Teams:Analysis, Review, and Challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.Comment: Engineering Applications of Artificial Intelligence, 201

Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making

International audienceIn this article, we propose an agent-based model of opinion diffusion and voting where influence among individuals and deliberation in a group are mixed. The model is inspired from social modeling, as it describes an iterative process of collective decision-making that repeats a series of interindividual influences and collective deliberation steps, and studies the evolution of opinions and decisions in a group. It also aims at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and ABM-influence models, which are not obvious to combine as a formal link between them is required. In our model, we find that deliberation, through the exchange of arguments, reduces the variance of opinions and the proportion of extremists in a population as long as not too much deliberation takes place in the decision processes. Additionally, if we define the correct collective decisions in the system in terms of the arguments that should be accepted, allowing for more deliberation favors convergence towards the correct decisions

MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library.

Multi-Agent Reinforcement Learning (MARL) en-compasses a powerful class of methodologies that have beenapplied in a wide range of fields. An effective way to furtherempower these methodologies is to develop approaches and toolsthat could expand their interpretability and explainability. Inthis work, we introduce MARLeME: a MARL model extractionlibrary, designed to improve explainability of MARL systemsby approximating them with symbolic models. Symbolic modelsoffer a high degree of interpretability, well-defined properties,and verifiable behaviour. Consequently, they can be used toinspect and better understand the underlying MARL systemsand corresponding MARL agents, as well as to replace all/someof the agents that are particularly safety and security critical.In this work, we demonstrate how MARLeME can be appliedto two well-known case studies (Cooperative Navigation andRoboCup Takeaway), using extracted models based on AbstractArgumentation

Business Intelligence Expert System on SOX Compliance over the Purchase Orders Creation Process

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility and quality of the Purchase Orders Creation Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques. This proposed model directly contributes to both scientific research artificial intelligent area and business practices. From business perspective it empowers the use of artificial intelligent models and techniques to drive decision making processes over financial statements. From scientific and research area the impact is based on the combination of 1) an Information Seeking Dialog Protocol in which a requestor agent inquires the business case; 2) a Facts Valuation based Protocol in which the previously gathered facts are analyzed; 3) the already incorporated initial knowledge of a human expert via initial beliefs; 4) the Intra-Agent Decision Making Protocol based on deductive argumentation; and 5) the semi automated Dynamic Knowledge Learning Protocol. Last but not least the suggested way of integration of this proposed model in a higher level multiagent intelligent system in which a Joint Deliberative Dialog Protocol and an Inter-Agent Decision Deductive Argumentation Making Protocol are described