OperA/ALIVE/OperettA

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft

OWL-POLAR : semantic policies for agent reasoning

The original publication is available at www.springerlink.comPostprin

OWL-POLAR : A Framework for Semantic Policy Representation and Reasoning

Peer reviewedPreprin

Regulated MAS: Social Perspective

This chapter addresses the problem of building normative multi-agent systems in terms of regulatory mechanisms. It describes a static conceptual model through which one can specify normative multi-agent systems along with a dynamic model to capture their operation and evolution. The chapter proposes a typology of applications and presents some open problems. In the last section, the authors express their individual views on these mattersMunindar Singh’s effort was partially supported by the U.S. Army Research Office under grant W911NF-08-1-0105. The content of this paper does not necessarily reflect the position or policy of the U.S. Government; no official endorsement should be inferred or implied. Nicoletta Fornara’s effort is supported by the Hasler Foundation project nr. 11115-KG and by the SER project nr. C08.0114 within the COST Action IC0801 Agreement Technologies. Henrique Lopes Cardoso’s effort is supported by Fundação para a Ciência e a Tecnologia (FCT), under project PTDC/EIA-EIA/104420/2008. Pablo Noriega’s effort has been partially supported by the Spanish Ministry of Science and Technology through the Agreement Technologies CONSOLIDER project under contract CSD2007-0022, and the Generalitat of Catalunya grant 2009-SGR-1434.Peer Reviewe

An Approach to Operationalize Regulative Norms in Multiagent Systems

International audienc

Creativity and Culture in Copyright Theory

Creativity is universally agreed to be a good that copyright law should seek to promote, yet copyright scholarship and policymaking have proceeded largely on the basis of assumptions about what it actually is. When asked to discuss the source of their inspiration, individual artists describe a process that is intrinsically ineffable. Rights theorists of all varieties have generally subscribed to this understanding, describing creativity in terms of an individual liberty whose form remains largely unspecified. Economic theorists of copyright work from the opposite end of the creative process, seeking to divine the optimal rules for promoting creativity by measuring its marketable byproducts. But these theorists offer no particular reason to think that marketable byproducts are either an appropriate proxy or an effective stimulus for creativity (as opposed to production), and more typically refuse to engage the question. The upshot is that the more we talk about creativity, the more it disappears from view. At the same time, the mainstream of intellectual property scholarship has persistently overlooked a broad array of social science methodologies that provide both descriptive tools for constructing ethnographies of creative processes and theoretical tools for modeling them

A Conceptual Model for Situating Purposes in Artificial Institutions

{In multi-agent systems, artificial institutions connect institutional concepts, belonging to the institutional reality, to the concrete elements that compose the system. The institutional reality is composed of a set of institutional concepts, called Status-Functions. Current works on artificial institutions focus on identifying the status-functions and connecting them to the concrete elements. However, the functions associated with the status-functions are implicit. As a consequence, the agents cannot reason about the functions provided by the elements that carry the status-functions and, thus, cannot exploit these functions to satisfy their goals. Considering this problem, this paper proposes a model to express the functions -- or the purposes -- associated with the status-functions. Examples illustrate the application of the model in a practical scenario, showing how the agents can use purposes to reason about the satisfaction of their goals in institutional contexts

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

Hierarchical agent supervision

Agent supervision is a form of control/customization where a supervisor restricts the behavior of an agent to enforce certain requirements, while leaving the agent as much autonomy as possible. To facilitate supervision, it is often of interest to consider hierarchical models where a high level abstracts over low-level behavior details. We study hierarchical agent supervision in the context of the situation calculus and the ConGolog agent programming language, where we have a rich first-order representation of the agent state. We define the constraints that ensure that the controllability of in-dividual actions at the high level in fact captures the controllability of their implementation at the low level. On the basis of this, we show that we can obtain the maximally permissive supervisor by first considering only the high-level model and obtaining a high- level supervisor and then refining its actions locally, thus greatly simplifying the supervisor synthesis task