Editor’s Note

This special issue “Artificial Intelligence and Social Application” includes extended versions of selected papers from Artificial Intelligence and Education area of the 13th edition of the Ibero-American Conference on Artificial Intelligence, held in Cartagena de Indias - Colombia, November, 2012. The issue includes, thus, five selected papers, describing innovative research work, on Artificial Intelligence in Education area including, among others: Recommender Systems, Learning Objects, Intelligent Tutoring Systems, Multi-Agent Systems, Virtual Learning Environments, Case-based reasoning and Classifiers Algorithms. This issue also includes six papers in the Interactive Multimedia and Artificial Intelligence areas, dealing with subjects such as User Experience, E-Learning, Communication Tools, Multi-Agent Systems, Grid Computing. IBERAMIA 2012 was the 13th edition of the Ibero-American Conference on Artificial Intelligence, a leading symposium where the Ibero-American AI community comes together to share research results and experiences with researchers in Artificial Intelligence from all over the world. The papers were organized in topical sections on knowledge representation and reasoning, information and knowledge processing, knowledge discovery and data mining, machine learning, bio-inspired computing, fuzzy systems, modelling and simulation, ambient intelligence, multi-agent systems, human-computer interaction, natural language processing, computer vision and robotics, planning and scheduling, AI in education, and knowledge engineering and applications

Distributed reinforcement learning in multi-agent decision systems

Proceeding of: 6th Ibero-American Conference on AI (IBERAMIA '98),Lisbon, Portugal, October 5–9, 1998Decision problems can be usually solved using systems that implement different paradigms. These systems may be integrated into a single distributed system, with the expectation of obtaining a group performance more satisfactory than individual performances. Such a distributed system is what we call a Multi Agent Decision System (MADES), a special kind of Multi Agent System, that integrates several heterogeneous autonomous decision systems (agents). A MADES must produce a single solution proposal for the problem instance it faces, despite the fact that its decision making is distributed, and every agent produces solution proposals according to its local view and to its idiosyncrasy. We present a distributed reinforcement algorithm for learning how to combine the decisions the agents make in a distributed way, into a single group decision (solution proposal).Publicad

Genetic programming for predicting protein networks

Proceeding of: 11th Ibero-American Conference on AI (IBERAMIA 2008), Lisbon, Portugal, 14-17 Octubre 2008One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility, particularly, the possibility of defining new operations. In this paper, the missing values problem benefits from the definition of if-unknown, a new operation which is more appropriate to the domain data semantics. Besides, in order to improve the solution size and the computational time, we use the Tarpeian method which controls the bloat effect of GP. According to the obtained results, we have verified the feasibility of using GP in this domain, and the enhancement in the search efficiency and interpretability of solutions due to the Tarpeian method.Publicad

Improving conflict support environments with information regarding social relationships

"Advances in artificial intelligence : IBERAMIA 2014 : 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, proceedings", ISBN 978-3-319-12026-3Having knowledge about social interactions as a basis for informed decision support in situations of conflict can be determinant. However, lower attention is given to the social network interpretation process in conflict management approaches. The main objective of the work presented here is to identify how the parties’ social networks correlate to their negotiation performance and how this can be formalized. Therefore, an experiment was set up in which was tried to streamline all the relevant aspects of the interaction between the individual and its environment that occur in a rich sensory environment (where the contextual modalities were monitored). This research explicitly focuses on the idea that an Ambient Intelligence system can create scenarios that augment the possibilities of reaching a positive outcome taking into account the role of contextualized social relationships in various conflict management strategies.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundac¸ ˜ao para a Ciˆencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014

Novelty Search in Competitive Coevolution

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014

Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

Epidemiological early warning systems for dengue fever rely on up-to-date epidemiological data to forecast future incidence. However, epidemiological data typically requires time to be available, due to the application of time-consuming laboratorial tests. This implies that epidemiological models need to issue predictions with larger antecedence, making their task even more difficult. On the other hand, online platforms, such as Twitter or Google, allow us to obtain samples of users' interaction in near real-time and can be used as sensors to monitor current incidence. In this work, we propose a framework to exploit online data sources to mitigate the lack of up-to-date epidemiological data by obtaining estimates of current incidence, which are then explored by traditional epidemiological models. We show that the proposed framework obtains more accurate predictions than alternative approaches, with statistically better results for delays greater or equal to 4 weeks.Comment: ACM Digital Health 201

A Generic Agent Organisation Framework For Autonomic Systems

Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems

Prompt to GPT-3: Step-by-Step Thinking Instructions for Humor Generation

Artificial intelligence has made significant progress in natural language processing, with models like GPT-3 demonstrating impressive capabilities. However, these models still have limitations when it comes to complex tasks that require an understanding of the user, such as mastering human comedy writing strategies. This paper explores humor generation using GPT-3 by modeling human comedy writing theory and leveraging step-by-step thinking instructions. In addition, we explore the role of cognitive distance in creating humor.Comment: 5 pages, 1 figure; ICCC '23 preprin

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-016-1065-xReducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.Peer ReviewedPostprint (author's final draft