Luís Morgado

RFID together with multi-agent systems to control global value chains

Abstract Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex suppliers network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.

Emotion based adaptive reasoning for resource bounded agents

In the design of resource bounded agents, high-level cognitive activities, such as reasoning, raise important problems related both to the adaptive ability and to the computational complexity of the underlying cognitive processes. To address these problems, we adopt an agent model where emotion and cognition are conceived as two integrated aspects of intelligent behavior and we present affective-emotional mechanisms that support the adaptation to changing environments and a controlled use of resources. These mechanisms produce an attention field that constrains the input to reasoning processes and also regulate the activation period of those processes. Experimental results are presented to illustrate this approach and to evaluate it by comparison with reference results concerning intention reconsideration policies.

Emotion in intelligent virtual agents: The flow model of emotion

Different models have been proposed to support emotion in artificial agents. However, a general framework to support the implementation of emotional agents of different kinds and levels of complexity is still not well de-fined. In this paper we present a model that is independent of specific physiological or psychological details, despite being inspired by biological processes, defining an emotional structure that can be objectively implemented and evaluated, and which can be used to integrate and extend other agent models, like deliberative models. Concrete results are presented to illustrate the model adequacy for agent emotional characterization.

Intelligent Transport System Based on RFID and Multi-Agent Approaches

This paper presents an Intelligent Information and Communication Technology (IICT) architecture able to cope with the nowadays logistics operators challenges. The aim is to achieve an Intelligent Transport System based on RFID together with Multi-agent systems. Furthermore, the logistical platforms (production or distribution), as nodes of added value of supplying and distribution networks, are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.

Adaptation and decision-making driven by emotional memories

The integration between emotion and cognition can provide an important support for adaptation and decision-making under resource-bounded conditions, typical of real-world domains. The ability to adjust cognitive activity and to take advantage of emotion-modulated memories are two main aspects resulting from that integration. In this paper we address those issues under the framework of the agent flow model, describing the formation of emotional memories and the regulation of their use through attention focusing. Experimental results from simulated rescue scenarios show how the proposed approach enables effective decision making and fast adaptation rates in completely unknown environments.

Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms

In domains where time and resources are limited, the ability to balance resource consumption according to the problem characteristics and to the required solution quality is a crucial aspect of intelligent behavior. Growing evidence indicates that emotional phenomena may play an important role in that balance. To support this view we propose an agent model where emotion and reasoning are conceived as two symbiotically integrated aspects of cognitive processing. In this paper we concretize this view by extending emotion-based regulation of cognitive activity to enable an active control of the abstraction level at which cognitive processes operate through emotion-based attention mechanisms, thus allowing a dynamical adjustment of the resources used. Experimental results are presented to illustrate the proposed approach and to evaluate its effectiveness in a scenario where reasoning under time-limited conditions in a dynamic environment is required.

A signal based approach to artificial agent modeling

In this paper we propose an approach to agent modeling that follows a signal based metaphor where agents are modeled as dissipative structures and their cognitive structures are modeled as compositions of multiple energetic potentials. This uniform representational support is used to model both reactive and deliberative processes. To illustrate the descriptive adequacy of the model, two experimental cases are presented where reactive and deliberative processes are modeled based on the proposed approach.

Emotion Based Control of Reasoning and Decision Making

In real-world domains, where uncertainty and dynamism are pervasive and time and resources are limited, reasoning and decision-making processes raise important problems related both to adaptive ability and to the computational complexity of the underlying cognitive mechanisms. In this context the integration between emotion based mechanisms and cognitive mechanisms can play a key role to support the development of intelligent agents able of effective behavior under real-time resource-bounded conditions. In this paper we address those issues under the framework of the agent flow model, an agent model where emotion and cognition are modeled as two integrated aspects of intelligent behavior and where affective- emotional mechanisms are used to support adaptability and to focus the reasoning and deliberation mechanisms to cope with their computational complexity.

Modeling the Dynamic Nonlinear Nature of Emotional Phenomena

The study of emotional phenomena, particularly the development of emotion models for intelligent agent implementation, has been mainly based on a perspective of emotion as a human phenomenon and on the assumption that emotions can be divided into discrete and independent categories. We propose an alternative model that emphasizes the continuous nonlinear nature of emotion processes, where emotional phenomena are rooted not on high level cognitive or even nervous structures, but on biophysical principles that are pervasive among biological organisms.