A methodology to engineer graded BDI agents

In this work we present a methodological framework to engineer graded BDI agent-based systems. The graded BDI agent model allows to specify an agent’s architecture able to deal with the environment uncertainty and with graded mental attitudes. We work up previous approaches on software engineering process, adapting and extending them, in order to develop agents with a more complex internal architecture.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Modeling travel assistant agents: a graded BDI approach

In this paper, we use a graded BDI agent model based on multi-context systems to specify an architecture for a Travel Assistant Agent that helps a tourist to choose holiday packages. We outline the theories of the different contexts and the bridge rules and illustrate the overall reasoning process of our model.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 2Red de Universidades con Carreras en Informática (RedUNCI

Modeling travel assistant agents: a graded BDI approach

In this paper, we use a graded BDI agent model based on multi-context systems to specify an architecture for a Travel Assistant Agent that helps a tourist to choose holiday packages. We outline the theories of the different contexts and the bridge rules and illustrate the overall reasoning process of our model.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 2Red de Universidades con Carreras en Informática (RedUNCI

A language for the execution of graded BDI agents

We are interested in the specification and deployment of multi-agent systems, and particularly we focus on the execution of agents. Along this research line, we have proposed a general model for graded BDI agents, specifying an architecture based on multi-context systems (MCSs) and able to deal with the environment uncertainty (via graded beliefs) and with graded mental proactive attitudes (via desires and intentions). These graded attitudes are represented using appropriate fuzzy modal logics. In this article, we cope with the operational semantics of this agent model. We present a Multi-context calculus, based on Ambient calculus, for the execution of MCSs with its corresponding semantics. This calculus is general enough to support different kinds of MCSs and particularly, we show how a graded BDI agent can be mapped into the language of the calculus. © The Author 2011. Published by Oxford University Press. All rights reserved.The authors are thankful to the anonymous reviewers for their helpful comments for improving the paper. Ana Casali acknowledge partial support by the PID-UNR ING308 project. Llus Godo and Carles Sierra acknowledge partial support by the Spanish project Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010).Peer Reviewe

Weighted logics for artificial intelligence : an introductory discussion

International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models. © 2010 The Author(s).This work was supported by the projects AEI (TIN2006-15662-C02-01), AT (CONSOLIDER CSD20070022, INGENIO 2010), LiquidPub (STREP FP7-213360), RepBDI (Intramural 200850I136) and by the Generalitat de Catalunya under the grant 2005-SGR-00093.Peer Reviewe

Rational Requirements and the Primacy of Pressure

There are at least two threads in our thought and talk about rationality, both practical and theoretical. In one sense, to be rational is to respond correctly to the reasons one has. Call this substantive rationality. In another sense, to be rational is to be coherent, or to have the right structural relations hold between one’s mental states, independently of whether those attitudes are justified. Call this structural rationality. According to the standard view, structural rationality is associated with a distinctive set of requirements that mandate or prohibit certain combinations of attitudes, and it’s in virtue of violating these requirements that incoherent agents are irrational. I think the standard view is mistaken. The goal of this paper is to explain why, and to motivate an alternative account: rather than corresponding to a set of law-like requirements, structural rationality should be seen as corresponding to a distinctive kind of pro tanto rational pressure—i.e. something that comes in degrees, having both magnitude and direction. Something similar is standardly assumed to be true of substantive rationality. On the resulting picture, each dimension of rational evaluation is associated with a distinct kind of rational pressure—substantive rationality with (what I call) justificatory pressure and structural rationality with attitudinal pressure. The former is generated by one’s reasons while the latter is generated by one’s attitudes. Requirements turn out to be at best a footnote in the theory of rationality

Reasoning about norms under uncertainty in dynamic environments

The behaviour of norm-autonomous agents is determined by their goals and the norms that are explicitly represented inside their minds. Thus, they require mechanisms for acquiring and accepting norms, determining when norms are relevant to their case, and making decisions about norm compliance. Up un- til now the existing proposals on norm-autonomous agents assume that agents interact within a deterministic environment that is certainly perceived. In prac- tice, agents interact by means of sensors and actuators under uncertainty with non-deterministic and dynamic environments. Therefore, the existing propos- als are unsuitable or, even, useless to be applied when agents have a physical presence in some real-world environment. In response to this problem we have developed the n-BDI architecture. In this paper, we propose a multi -context graded BDI architecture (called n-BDI) that models norm-autonomous agents able to deal with uncertainty in dynamic environments. The n-BDI architecture has been experimentally evaluated and the results are shown in this paper.This paper was partially funded by the Spanish government under Grant CONSOLIDER-INGENIO 2010 CSD2007-00022 and the Valencian government under Project PROMETEOH/2013/019.Criado Pacheco, N.; Argente, E.; Noriega, P.; Botti Navarro, VJ. (2014). Reasoning about norms under uncertainty in dynamic environments. International Journal of Approximate Reasoning. 55(9):2049-2070. https://doi.org/10.1016/j.ijar.2014.02.004S2049207055

CAN(PLAN)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent's beliefs. These epistemic states are stratified to make them commensurable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient semantics. Finally, we examine how primitive actions are affected by uncertainty and we define an appropriate form of look ahead planning.This research is supported by the UK EPSRC project EP/J012149/1.Peer Reviewe