Abstracts:

Our reasoning process uses and is influenced by our perception model of the environment stimuli and by our memorization of related experiences, beliefs, and emotions that are associated with each stimulus, whilst taking in considerations other factors such as time and space. These two processes of modelling and memorization happen in real time while interspersing with each other in a manner they almost seem as if they are one process. This is often referred to as cognition. In this paper we provide a simplified model of this complicated relationship between emotions, perceptions and our behaviour to produce a model that can be used in software agents and humanized robots. (pp. 119-126)

Keywords: Connectionism, Cognitive Maps, Perception, Emotions, Automated Reasoning, Reasoning about Actions

This paper proposes a model of emotionally influenced perception in an affective agent. Via a multidimensional representation of emotion, a mechanism called the affective space is used to emotionally filter sensed stimuli in the agent’s environment. This filtering process allows different emotional states in the agent to create dissimilar emotional reactions when the agent is exposed to the same stimuli. In humans, emotion cannot be a mechanism that enhances intelligence and then isolated from other psychological and physiological functions. As this paper suggests, emotion is an integral part of the human as a biological being and therefore they cannot be turned on an off on a whim. However, this luxury is afforded to the artificial agent. This approach builds on contemporary affective agent architectural concepts as it not only gives an agent the ability to use emotion to produce human-like intelligence, but it also investigates how emotion should affect the agent’s other abilities, such as perception. (pp. 127-136)

Keywords: agents, emotion, perception, decision-making, affective computing

A new approach for the control of dynamical systems is presented based on the agent concept. The control system consists of a set of neurofuzzy controllers whose weights are adapted according to emotional signals provided by blocks called emotional critics. Simulation results are provided for the control of dynamical systems with various complexities in order to show the effectiveness of the proposed method. (pp. 137-144)

An emotional architecture for artificial agents, inspired in Damásio's concept of somatic markers, is described in this paper. An associative memory, capable of providing estimates of action consequences for given situations, is also developed. The role emotions play on behaviour triggering is also discussed. This architecture has been successfully applied to robotic soccer, leading to an effective emotion-based machine learning. (pp. 145-156)

Darryl N. Davis and Suzanne C. Lewis
Department of Computer Science, University of Hull
Cottingham Road, Kingston-upon-Hull, UK, HU6 7RX
E-mail: D.N.Davis@dcs.hull.ac.uk, S.C.Lewis@dcs.hull.ac.uk
Website: http://www2.dcs.hull.ac.uk/NEAT/dnd/index.htm, http://www2.dcs.hull.ac.uk/NEAT/index.html

Recent evidence suggests that the emotions play a crucial role in perception, learning and rational decision making. Despite arguments to the contrary, all artificial intelligent systems are, to some extent, autonomous. This research investigates how emotion can be used as the basis for autonomy. We propose the use of an emotion-based control language that maps over all layers of a computational architecture. We report on how theoretical work and both design and computational experiments with this concept are being used to direct perception, behavior selection and reasoning in cognitive agents. (pp. 157-164)

Neural networks and Neurofuzzy models have been successfully used in the prediction of nonlinear time series. Several learning methods have been introduced to train the Neurofuzzy predictors, such as ANFIS, ASMOD and FUREGA. Many of these methods, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. However, they differ in computational complexity. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the prediction of real world data. For example, predicting the peaks of sunspot numbers (maximum of solar activity) is more important due to its major effects on earth and satellites. The emotional learning based fuzzy inference system (ELFIS) has the advantages of simplicity and low computational complexity in comparison with other multi-objective optimization methods. The efficiency of proposed predictor is shown in two examples of highly nonlinear time series. Appropriate emotional signal is composed for the prediction of solar activity and price of securities. It is observed that ELFIS performs better predictions in the important regions of solar maximum, and is also a fast and efficient algorithm to enhance the performance of ANFIS predictor in both examples. (pp. 165-174)

The purpose of the work reported here is the development of an autonomous robot controller which learns to perform a multi-goal and multi-step task when faced with real world problems such as continuous time and space, noisy sensors and unreliable actuators. In order to make the learning task feasible, the agent does not have to learn its action abilities from scratch, but relies on a small set of simple hand-designed behaviors. Experience has shown that these low-level behaviors can be either easily designed or learned but that the coordination of these behaviors is not trivial. To solve the problem at hand, a dual-system architecture is proposed in which a traditional reinforcement learning adaptive system is complemented with a goal system responsible for both reinforcement and behavior switching. This goal system is inspired by emotions, which take a functional role on this work, and are evaluated in terms of their engineering benefits, i.e. in terms of their competitiveness when compared with alternative approaches. Experiments reported carefully evaluate the goal system and determine its requirements. (pp. 175-184)

Keywords: learning, emotions, autonomous robots

Marcia Maçãs, Luis Custódio
Institute for Systems and Robotics, Instituto Superior Técnico,
Lisbon, Portugal
E-mail: marcia@isr.ist.utl.pt, lmmc@isr.ist.utl.pt
Website: http://www.isr.ist.utl.pt/~islab

The role of emotions in human intelligence and social behaviours has been considered very important in the past years. The DARE architecture, an emotion-based agent architecture, aims at the modelling of this contribution for building autonomous agents. In this paper the results of its application to a multiple agents
environment are presented. Emotions are used at an individual decision level, through the modelling of the somatic marker hypothesis, and are also used on decisions that involve others, using the same hypothesis and adding the notion of sympathy. The representation of other agents external expression allows to predict their internal state. This process is based on the assumption that similar agents express their internal state in similar way, being a mean of implicit communication. Sympathy allows more informed individual decisions, specially when these depend on others. On the other hand it makes agents learn, not only based on their own experience, but also with others experience. Besides implicit communication, it is also used explicit communication, through messages exchanging. In the symbolic layer, a new layer added to the DARE architecture, interactions between agents are represented and used to improve individual and social behaviours. (pp. 185-196)

Keywords: agent architecture, emotions, society of agents

Mark Neal
Department of Computer Science, University of Wales
Aberystwyth, UK
E-mail: mjn@aber.ac.uk
AND
Jon Timmis
Computing Laboratory, University of Kent
Canterbury, UK
E-mail: J.Timmis@kent.ac.uk

Responses labelled as emotional in the higher animals are frequently portrayed as incidental to the generation of reasonable behavior. Clearly this view is incompatible with the reality of animal behavior as observed in nature, emotion plays a significant role in the generation of useful behaviour. Homeostasis is the product of the interaction of the nervous, endocrine and immune systems. This work views emotional responses as part of an integrated approach to the generation of behavior in artificial organisms via mechanisms inspired by homeostasis. The mechanism presented here employs the concept of a novel Artificial Endocrine System which interacts with an Artificial Neural Network to generate behaviour which could be classified as emotive. (pp. 197-204)

Keywords: endocrine system, artificial endocrine systems, neural networks, robot behaviour, emotion, perception, homeostasis

Rafal Rzepka and Kenji Araki
Graduate School of Engineering, Hokkaido University
Kita-ku, Kita 13-jo Nishi 8-chome, 060-8628 Sapporo, Japan
kabura@media.eng.hokudai.ac.jp, araki@media.eng.hokudai.ac.jp
Website: http://sig.media.eng.hokudai.ac.jp/index_e.html
AND
Koji Tochinai
Graduate School of Business Administration, Hokkai-Gakuen University
Toyohira-ku, Asahi-machi 4-1-40, 062-8605 Sapporo, Japan
E-mail: tochinai@econ.hokkai-s-u.ac.jp

In our project (GENTA - GENeral belief reTrieving Agent), we are trying to realize a conversational agent, which would be able to talk in any domain by using web-mining techniques to retrieve information that is impossible to obtain in usually used corpora. In our research we try to simulate reasoning processes based on Internet textual resources including chat logs. Our goal is a dialogue system which learns the linguistic behaviour of an interlocutor concentrating on the role of emotion during analysing discourse. The system is not using any databases of commonsensical word descriptions, they are being automatically retrieved from the WWW. We describe two values called Positiveness and Usualness and explain their role in the Inductive Learning that is used for achieving emotion-based reasoning skills. As this is a new approach to knowledge acquisition for dialogue agents we concentrate on the theoretical part of our project. Finally we introduce the results of the preliminary experiments. (pp. 205-212)

Keywords: natural language processing, spoken dialog agents, affective computing

Zina Ben Miled
Electrical & Computer Engineering, Purdue School of Eng. & Tech.
Indianapolis, IN 46202
AND
Huian Li and Omran Bukhres
Computer & Information Science, Purdue School of Science
Indianapolis, IN 46202
AND
Michael Bem, Robert Jones and Robert Oppelt
Eli Lilly and Company
Indianapolis, IN 46202

Pharmaceutical drug candidate databases have reached massive sizes in recent years due to the improvement of benchside high throughput screening tools used by scientists. This rapid increase has caused a shift in the bottleneck in discovery and product development from the benchside to the computational side, thus creating a need for new computational tools that can facilitate the access and interpretation of such massive data. In this paper, a window-based compression technique that supports random database access is introduced. This technique improves random access to records in the database while maintaining high sequential throughput. The impact of the proposed compression technique is evaluated in the context of a non-indexed and an indexed database. The performance gain of the window-based compression technique is demonstrated using a drug candidate database which is used in the pharmaceutical drug
discovery process. (pp. 213-224)

Boštjan Berèiè
Institute for Legal Informatics
1000 Ljubljana, Slovenia
E-mail: bostjan.bercic@ipri-zavod.si

Modelling of legal acts often felt short of expectations because it didn't take into account legal theory. This paper proposes a different approach to modelling that is based on the theory of law. Legal theory (structure, hierarchy and types of legal rules) is considered a fundament and then interpreted with expert systems, high-level Petri nets and ECA rules. In particular, none of these methods alone is sufficiently strong to capture the semantics of legal rules. Put together, they represent a powerful means to overcome some difficulties legal modelers have encountered in the past. This paper takes into account procedural and substantive aspects of law, as well as factual and deontic ones. Methodology is presented alongside with notation and examples to clarify the idea. (pp. 225-234)

Keywords: expert systems, petri nets, legal informatics