A context aware architecture to support people with partial visual impairments

Nowadays there are several systems that help people with disabilities on their quotidian tasks. The visual impairment is a problem that affects several people in their tasks and movements. In this work we propose an architecture capable of processing information from the environment and suggesting actions to the user with visual impairments, to avoid a possible obstacle. This architecture intends to improve the support given to the user in their daily movements. The idea is to use speculative computation to predict the users’ intentions and even to justify the reactive or proactive users’ behaviors.(undefined

Modelling an orientation system based on speculative computation

Progress is inherent to a living society, which may occur in several different areas (e.g. computation, healthcare) and manners. The present (or now) is the time that is associated with the events perceived directly and in the first time, making, for example, the society to be very inquisitive on assistive technologies and how they may improve the human beings quality of living. This application of scientific knowledge for practical purposes may help the user in his/her diminished capabilities, and, usually, implies a small adaptation on the part of the individual in the use of devices; indeed one of the die down potentials of people with cognitive disabilities is the one of spatial orientation. On the other hand several were the authors that have developed systems to help an human being to travel between two locations. However, once the system is set up the change in the configurations have to be done manually in order to better adjust the system to the user. In order to go round this drawback, in this work it is presented a framework of speculative computation to set up the computation of the next user step using default values. When the information is obtained the computation is revised. Thus, the system may have a faster reaction to the user stimulus or it may start warning the user before he/she takes the wrong direction.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/EEISII/ 1386/2012). The work of Jo˜ao Ramos is supported by a doctoral grant by FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) SFRH/BD/89530/- 2012

Multi-agent Confidential Abductive Reasoning

In the context of multi-agent hypothetical reasoning, agents typically have partial knowledge about their environments, and the union of such knowledge is still incomplete to represent the whole world. Thus, given a global query they collaborate with each other to make correct inferences and hypothesis, whilst maintaining global constraints. Most collaborative reasoning systems operate on the assumption that agents can share or communicate any information they have. However, in application domains like multi-agent systems for healthcare or distributed software agents for security policies in coalition networks, confidentiality of knowledge is an additional primary concern. These agents are required to collaborately compute consistent answers for a query whilst preserving their own private information. This paper addresses this issue showing how this dichotomy between "open communication" in collaborative reasoning and protection of confidentiality can be accommodated. We present a general-purpose distributed abductive logic programming system for multi-agent hypothetical reasoning with confidentiality. Specifically, the system computes consistent conditional answers for a query over a set of distributed normal logic programs with possibly unbound domains and arithmetic constraints, preserving the private information within the logic programs. A case study on security policy analysis in distributed coalition networks is described, as an example of many applications of this system

Logics and Multi-agents: towards a new symbolic model of cognition

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Conditional Answer Computation in SOL as Speculative Computation in Multi-Agent Environments1 1This research was supported partly by Grant-in-Aid from The Ministry of Education, Science and Culture of Japan.

AbstractIn this paper, we study speculative computation in a master-slave multi-agent system where reply messages sent from slave agents to a master are always tentative and may change from time to time. In this system, default values used in speculative computation are only partially determined in advance. Inoue et al. [8] formalized speculative computation in such an environment with tentative replies, using the framework of a first-order consequence-finding procedure SOL with the well-known answer literal method. We shall further refine the SOL calculus, using conditional answer computation and skip-preference in SOL. The conditional answer format has an great advantage of explicitly representing how a conclusion depends on tentative replies and defaults, both of which are used to derive the conclusion. The dependency representation is significantly important to avoid unnecessary recomputation of tentative conclusions. The skip-preference has the great ability of preventing irrational/redundant derivations

FutureCrafting. A Speculative Method for an Imaginative AI

The issue I explore with this position paper concerns dominant cultural scripts around Artificial Intelligence (AI) and the need to imagine different narratives in light of machine learning’s autonomous performativity. The aim is to offer a philosophical reflection, not only to sidestep narratives of techno-determinism, dystopia and existential risk to mankind, but also to speculate on how to imagine a (more) benevolent AI based on uncertainty and the co-evolution of humans and technology. The paper presents the speculative methodology I call FutureCrafting: a forensic, diagnostic and divinatory method that investigates the possibility of other discourses, equally powerful in building reality, constructing futures and having tangible impact. FutureCrafting is speculation at the juncture of design and philosophy, pivoting around the open-ended figuration of the what if…? It articulates collaboration rather than competition, coevolution rather than antagonism, and privileges the indeterminate and the imaginative. To conclude, the paper makes reference to the non-human intelligence of the octopus and to how this can inform a more imaginative AI

Distributed Abductive Reasoning: Theory, Implementation and Application

Abductive reasoning is a powerful logic inference mechanism that allows assumptions to be made during answer computation for a query, and thus is suitable for reasoning over incomplete knowledge. Multi-agent hypothetical reasoning is the application of abduction in a distributed setting, where each computational agent has its local knowledge representing partial world and the union of all agents' knowledge is still incomplete. It is different from simple distributed query processing because the assumptions made by the agents must also be consistent with global constraints. Multi-agent hypothetical reasoning has many potential applications, such as collaborative planning and scheduling, distributed diagnosis and cognitive perception. Many of these applications require the representation of arithmetic constraints in their problem specifications as well as constraint satisfaction support during the computation. In addition, some applications may have confidentiality concerns as restrictions on the information that can be exchanged between the agents during their collaboration. Although a limited number of distributed abductive systems have been developed, none of them is generic enough to support the above requirements. In this thesis we develop, in the spirit of Logic Programming, a generic and extensible distributed abductive system that has the potential to target a wide range of distributed problem solving applications. The underlying distributed inference algorithm incorporates constraint satisfaction and allows non-ground conditional answers to be computed. Its soundness and completeness have been proved. The algorithm is customisable in that different inference and coordination strategies (such as goal selection and agent selection strategies) can be adopted while maintaining correctness. A customisation that supports confidentiality during problem solving has been developed, and is used in application domains such as distributed security policy analysis. Finally, for evaluation purposes, a flexible experimental environment has been built for automatically generating different classes of distributed abductive constraint logic programs. This environment has been used to conduct empirical investigation of the performance of the customised system

Multi-Agent Systems

[EN] With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.Julian Inglada, VJ.; Botti V. (2019). Multi-Agent Systems. Applied Sciences. 9(7):1-7. https://doi.org/10.3390/app9071402S1797Kravari, K., & Bassiliades, N. (2015). A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi:10.18564/jasss.2661Baldoni, M., Baroglio, C., May, K., Micalizio, R., & Tedeschi, S. (2018). Computational Accountability in MAS Organizations with ADOPT. Applied Sciences, 8(4), 489. doi:10.3390/app8040489Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747-761. doi:10.1016/j.scico.2011.10.004Challenger, M., Tezel, B., Alaca, O., Tekinerdogan, B., & Kardas, G. (2018). Development of Semantic Web-Enabled BDI Multi-Agent Systems Using SEA_ML: An Electronic Bartering Case Study. Applied Sciences, 8(5), 688. doi:10.3390/app8050688Challenger, M., Demirkol, S., Getir, S., Mernik, M., Kardas, G., & Kosar, T. (2014). On the use of a domain-specific modeling language in the development of multiagent systems. Engineering Applications of Artificial Intelligence, 28, 111-141. doi:10.1016/j.engappai.2013.11.012Boztepe, İ., & Erdur, R. (2018). Linked Data Aware Agent Development Framework for Mobile Devices. Applied Sciences, 8(10), 1831. doi:10.3390/app8101831Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365-377. doi:10.1016/j.artint.2006.02.006Duan, K., Fong, S., Zhuang, Y., & Song, W. (2018). Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences, 8(6), 862. doi:10.3390/app8060862Zhang, Q., Yao, J., Yin, Q., & Zha, Y. (2018). Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution. Applied Sciences, 8(7), 1077. doi:10.3390/app8071077Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298. doi:10.1016/j.pmcj.2009.04.001Kranz, M., Holleis, P., & Schmidt, A. (2010). Embedded Interaction: Interacting with the Internet of Things. IEEE Internet Computing, 14(2), 46-53. doi:10.1109/mic.2009.141Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Costa, A., Novais, P., Corchado, J. M., & Neves, J. (2011). Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic Journal of IGPL, 20(4), 689-698. doi:10.1093/jigpal/jzr021Tapia, D. I., & Corchado, J. M. (2009). An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care. International Journal of Ambient Computing and Intelligence, 1(1), 15-26. doi:10.4018/jaci.2009010102Barriuso, A., De la Prieta, F., Villarrubia González, G., De La Iglesia, D., & Lozano, Á. (2018). MOVICLOUD: Agent-Based 3D Platform for the Labor Integration of Disabled People. Applied Sciences, 8(3), 337. doi:10.3390/app8030337Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Applied Sciences, 8(3), 446. doi:10.3390/app8030446Ramos, J., Oliveira, T., Satoh, K., Neves, J., & Novais, P. (2018). Cognitive Assistants—An Analysis and Future Trends Based on Speculative Default Reasoning. Applied Sciences, 8(5), 742. doi:10.3390/app8050742SATOH, K. (2005). Speculative Computation and Abduction for an Autonomous Agent. IEICE Transactions on Information and Systems, E88-D(9), 2031-2038. doi:10.1093/ietisy/e88-d.9.2031Miyashita, K. (2017). Incremental Design of Perishable Goods Markets through Multi-Agent Simulations. Applied Sciences, 7(12), 1300. doi:10.3390/app7121300Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Roscia, M., Longo, M., & Lazaroiu, G. C. (2013). Smart City by multi-agent systems. 2013 International Conference on Renewable Energy Research and Applications (ICRERA). doi:10.1109/icrera.2013.6749783Lozano, Á., De Paz, J., Villarrubia González, G., Iglesia, D., & Bajo, J. (2018). Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems. Applied Sciences, 8(1), 67. doi:10.3390/app8010067Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Billhardt, H., Fernández, A., Lujak, M., & Ossowski, S. (2018). Agreement Technologies for Coordination in Smart Cities. Applied Sciences, 8(5), 816. doi:10.3390/app805081

An orientation method with prediction and anticipation features

Nowadays, progress is constant and inherent to a living society. This may occur in different arenas, namely in mathematical evaluation and healthcare. Assistive technologies are a topic under this evolution, being extremely important in helping users with diminished capabilities (physical, sensory, intellectual). These technologies assist people in tasks that were difficult or impossible to execute. A common diminished task is orientation, which is crucial for the user autonomy. The adaptation to such technologies should require the minimum effort possible in order to enable the person to use devices that convey assistive functionalities. There are several solutions that help a human being to travel between two different locations, however their authors are essentially concerned with the guidance method, giving special attention to the user interface. The CogHelper system aims to overcome these systems by applying a framework of Speculative Computation, which adds a prediction feature for the next user movement giving an anticipation ability to the system. Thus, an alert is triggered before the user turn towards an incorrect path. The travelling path is also adjusted to the user preferences through a trajectory mining module.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of João Ramos is supported by a doctoral the FCT grant SFRH/BD/89530/2012. The work of Tiago Oliveira is also supported by the FCT grant with the reference SFRH/BD/85291/2012info:eu-repo/semantics/publishedVersio