Andres Garcia-Camino

Open multi-agent systems are populated by self-interested agents, developed by different people using different languages and architectures. One way of making these agents conform to some intended collective purpose is to use norms to regulate their behaviour, i.e.: discourage, prevent and mitigate harmful behaviour and facilitate desirable interaction. In this thesis, we propose a framework —composed by a formal model, a distributed architecture and languages and algorithms for its implementation— that extends the notion of an Electronic Institution in order to manage norms in activities, manage the propagation of their consequences among simultaneous concurrent activities, and resolve possible normative conflicts induced by this propagation.

Social choice is the theory about collective decision towards social welfare starting from individual opinions, preferences, interests or welfare. The field of Computational Social Welfare is somewhat recent and it is gaining impact in the Artificial Intelligence community. Classical literature makes the assumption of single-peaked preferences, i.e. there exist a linear order in the preferences and there is a global maximum in this order. Recently, some theoretical results were published about Two-stage Approval Voting Systems (TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular Preferences (CPs) that I claim leads to research about Preferences Graphs and Preferences Multi-dimensional Functions in Polynomial time. The purpose of this paper is threefold: Firstly,

Regulation of Multi-Agent Systems (MAS) and Declarative Electronic Institutions (DEIs) was a multidisciplinary research topic of the past decade involving (Physical and Software) Agents and Law since the beginning, but recently evolved towards News-claimed Robot Lawyer since 2016. One of these first proposals of restricting the behaviour of Software Agentswas Electronic Institutions.However, with the recent reformulation of Artificial Neural Networks (ANNs) as Deep Learning (DL), Security, Privacy,Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of Artificial Neural Networks as Agent-based Training of a special type of regulated Artificial Neural Network that we call Institutional Neural Network (INN).The main purpose of this paper is to bring attention to Artificial Teaching (AT) and to give a tentative answer showing a proof-of-...

Regulation of Multi-Agent Systems (MAS) was a research topic of the past decade and one of these proposals was Electronic Institutions. However, with the recent reformulation of Artificial Neural Networks (ANN) as Deep Learning (DL), Security, Privacy, Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of ANN as Agent-based Training of a special type of regulated ANN that we call Institutional Neural Network. This paper introduces the former concept and provides I, a language previously used to model and extend Electronic Institutions, as a means to implement and regulate DL.

Norms explicitly represent prohibitions, permissions and obligations associated with software agents, changing as agents act and interact in pursuit of their goals. Norms provide means of regulating open and heterogeneous multi-agent systems; however, in order to truly reflect the nature of multi-agent systems, norms should be managed in a distributed fashion. A centralized account of norms creates a single point-of-failure and bottlenecks, and as a result fault-tolerance and scalability are jeopardized. The decentralized management of ...

Social choice is the theory about collective decision towards social welfare starting from individual opinions, preferences, interests or welfare. The field of Computational Social Welfare is somewhat recent and it is gaining impact in the Artificial Intelligence community. Classical literature makes the assumption of single-peaked preferences, i.e. there exist a linear order in the preferences and there is a global maximum in this order. Recently, some theoretical results were published about Two-stage Approval Voting Systems (TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular Preferences (CPs) that I claim leads to research about Preferences Graphs and Preferences Multi-dimensional Functions in Polynomial time. The purpose of this paper is threefold: Firstly,

Regulation of Multi-Agent Systems (MAS) and Declarative Electronic Institutions (DEIs) was a multidisciplinary research topic of the past decade involving (Physical and Software) Agents and Law since the beginning, but recently evolved towards News-claimed Robot Lawyer since 2016. One of these first proposals of restricting the behaviour of Software Agents was Electronic Institutions. However, with the recent reformulation of Artificial Neural Networks (ANNs) as Deep Learning (DL), Security, Privacy, Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of Artificial Neural Networks as Agent-based Training of a special type of regulated Artificial Neural Network that we call Institutional Neural Network (INN). The main purpose of this paper is to bring attention to Artificial Teaching (AT) and to give a tentative answer showing a proof-of-concept implementation of Regulated Deep Learning (RDL). This paper introduces the former concept and provides I, a language previously used to model declaratively and extend Electronic Institutions, as a means to regulate the execution of Artificial Neural Networks and their interactions with Artificial Teachers (ATs).

Social choice is the theory about collective decision towards social welfare starting from individual opinions, preferences, interests or welfare. The field of Computational Social Welfare is somewhat recent and it is gaining impact in the Artificial Intelligence community. Classical literature makes the assumption of single-peaked preferences, i.e. there exist a linear order in the preferences and there is a global maximum in this order. Recently, some theoretical results were published about Two-stage Approval Voting Systems (TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular Preferences (CPs) that I claim leads to research about Preferences Graphs and Preferences Multi-dimensional Functions in Polynomial time. The purpose of this paper is threefold: Firstly,

Regulation of Multi-Agent Systems (MAS) and Declarative Electronic Institutions (DEIs) was a multidisciplinary research topic of the past decade involving (Physical and Software) Agents and Law since the beginning, but recently evolved towards News-claimed Robot Lawyer since 2016. One of these first proposals of restricting the behaviour of Software Agents was Electronic Institutions. However, with the recent reformulation of Artificial Neural Networks (ANNs) as Deep Learning (DL) [1], Security, Privacy, Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of Artificial Neural Networks as Agent-based Training of a special type of regulated Artificial Neural Network that we call Institutional Neural Network (INN). The main purpose of this paper is to bring attention to Artificial Teaching (AT) and to give a tentative answer showing a proof-of-concept implementation of Regulated Deep Learning (RDL). This paper introduces the former concept and provides I, a language previously used to model declaratively and extend Electronic Institutions, as a means to regulate the execution of Artificial Neural Networks and their interactions with Artificial Teachers (ATs).

Equitable Institutions are (Social and Inclusive) Agreements, in Nash Equilibrium, on Rules. Absolute Majority discriminates, Less-Discriminatory Majority is the future. Current Democracy is Opinion-based, (Rules on acts)-Utilitarism should be based on Facts, i.e. Fact-Utilitiarism, Rule-Utilitiarism, Preference-Utilitiarism and their inter-relationships to have a better understanding of Social Contract Theory, e.g. in which cases an institution is better than other and why; or what is a Helpful Vote. Arrow's Impossibility Theorem assumes no kind of Utility is used, so the fore-mentioned approach would avoid it, especially for Social Choice Theory. So there a need for Social Utilitarism, i.e. Social Choice and Social Mechanism Design Theory in tandem that would avoid the limitations of Game Theory, thus starting the new field of Social Game Theory based on (Rules on acts)-Utilitarism, i.e. a new (Social Contract and Institutions) Theory.