Graça Gaspar

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.

Computing Repairs from Active Integrity Constraints

Repairing an inconsistent knowledge base is a well known problem for which several solutions have been proposed and implemented in the past. In this paper, we start by looking at databases with active integrity constraints - consistency requirements that also indicate how the database should be updated when they are not met - as introduced by Caroprese et al.We show that the different kinds of repairs considered by those authors can be effectively computed by searching for leaves of specific kinds of trees. Although these computations are in general not very efficient (deciding the existence of a repair for a given database with active integrity constraints is NP-complete), on average the algorithms we present make significant reductions on the number of nodes in the search tree. Finally, these algorithms also give an operational characterization of different kinds of repairs that can be used when we extend the concept of active integrity constraints to the more general setting of knowledge bases.

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.

Using cognition and learning to improve agents' reactions

This paper proposes an agent-architecture to deal with real-time problems where it is important both to react to constant changes in the state of the environment and to recognize the generic tendencies in the sequence of those changes. Reactivity must satisfy the need for immediate answers; cognition will enable the perception of medium and long time variations, allowing decisions that lead to an improved reactivity. Agents are able to evolve through an instance-based learning mechanism fed by the cognition process that allows them to improve their performance as they accumulate experience. Progressively, they learn to relate their ways of reacting (reaction strategies) with the general state of the environment. Using a simulation workbench that sets a distributed communication problem, different tests are made in an effort to validate our proposal and put it in perspective as a solution for other problems.

Information Flow within Relational Multi-context Systems

Multi-context systems (MCSs) are an important framework for heterogeneous combinations of systems within the Semantic Web. In this paper, we propose generic constructions to achieve specific forms of interaction in a principled way, and systematize some useful techniques to work with ontologies within an MCS. All these mechanisms are presented in the form of general-purpose design patterns. Their study also suggests new ways in which this framework can be further extended.

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.

Active Integrity Constraints for Multi-context Systems

We introduce a formalism to couple integrity constraints over general-purpose knowledge bases with actions that can be executed to restore consistency. This formalism generalizes active integrity constraints over databases. In the more general setting of multi-context systems, adding repair suggestions to integrity constraints allows defining simple iterative algorithms to find all possible grounded repairs --- repairs for the global system that follow the suggestions given by the actions in the individual rules. We apply our methodology to ontologies, and show that it can express most relevant types of integrity constraints in this domain.

Design Patterns for Description-Logic Programs

Originally proposed in the mid-90s, design patterns for software development played a key role in object-oriented programming not only in increasing software quality, but also by giving a better understanding of the power and limitations of this paradigm. Since then, several authors have endorsed a similar task for other programming paradigms, in the hope of achieving similar benefits. In this paper we present a set of design patterns for Mdl-programs, a hybrid formalism combining several description logic knowledge bases via a logic program. These patterns are extensively applied in a natural way in a large-scale example that illustrates how their usage greatly simplifies some programming tasks, at the level of both development and extension.