HoCaMA: Home Care Hybrid Multiagent Architecture

Fraile, Juan Antonio García; Bajo, Javier; Abraham, Ajith; Corchado, Juan M.

doi:10.1007/978-1-84882-599-4_12

Cited by 11 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although several promising prototypes have been done in academia [11,13,1,5], yet there are many challenges and hurdles to overcome before having a realistic homecare solution [12,9]. Based on our study, we observed that existing solutions have not addressed dynamic provisioning and its requirements as much as other challenges such as distributed and heterogeneous application service providers [23].…”

Section: Introductionmentioning

confidence: 88%

Dynamic Homecare Service Provisioning: A Field Test and Its Results

Zarghami

Zarifi

Sinderen

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Providing IT-based care support for elderly at home is proposed as a highly promising appraoch to address the aging population problem. With the emergence of homecare application service providers, a homecare system can be seen as a linked set of services. Configuring and composing existing homecare application services to create new homecare composite applications can reduce the application development cost. The idea even looks more promising if the service provisioning is dynamic, i.e., if applications can update their behaviors with respect to the contextual changes without or with minimum manpower. Dynamic service provisioning can play an important role to accept homecare systems in practical settings. This motivated us to develop a Dynamic Homecare Service Provisioning (DHSP) platform to address the homecare context changes in an effective and efficient manner. As a proof of concept, we have developed a software prototype of our platform. The prototype was subsequently used in a real-world field test at a care institution in the Netherlands to validate the approach. This paper describes the design of the field test and reflects on the outcome of the validation experiments.

show abstract

Section: Introductionmentioning

confidence: 88%

Dynamic Homecare Service Provisioning: A Field Test and Its Results

Zarghami

Zarifi

Sinderen

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Agent technology has been appointed as a proper technology for the support of AmI solutions [4,10,31]. In fact, agents show interesting characteristics for AmI environments since they are reactive, proactive and social [36].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions

Sánchez-Anguix

Valero

Julián

et al. 2013

Information Sciences

View full text Add to dashboard Cite

Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.

show abstract

“…Agent technology has been appointed as a proper technology for the support of AmI solutions (22,47,48 reducing the use of memory (also related with the number of offers sampled). Additionally, economic requirements (e.g., utility of the final agreement) should not be forgotten.…”

Section: Automated Negotiation and Ambient Intelligencementioning

confidence: 99%

Complex negotiations in multi-agent systems.

Anguix¹

View full text Add to dashboard Cite

Multi-agent systems (MAS) are distributed systems where autonomous entities called agents, either human or software, pursue their own goals. The MAS paradigm has been proposed as the appropriate modeling approach for the deployment of applications like electronic commerce, multi-robot systems, security applications, and so forth. In the MAS community, the vision of open multi-agent system, where heterogeneous agents can enter and leave the system dynamically, has gained strength as a potentially interesting modeling paradigm due to its conceptual relation with technologies like world wide web, grid computing, and virtual organizations. Given the heterogeneity and agent's self-interest, conflict is a candidate phenomenon to arise in multi-agent systems.In the last few years, the term agreement technologies has been used to address all the mechanisms that, directly or indirectly, promote the resolution of conflicts in computational systems like multi-agent systems. Among agreement technologies, automated negotiation is proposed as one key mechanism in conflict resolution due to its analogous use in human conflict resolution. Automated negotiation consists of an automated exchange of proposals carried out by software agents on behalf of their users. The final goal is the achievement of an agreement with all the involved parts.Despite being studied by scholars in Artificial Intelligence for several years, several problems have not been addressed by the scientific community yet. The main objective of this thesis is proposing negotiation models for complex scenarios where the complexity may stem from (i) limited computational capabilities or (ii) the necessity to accommodate the preferences of multiple individuales. In the first part of the thesis we propose a bilateral negotiation model for the problem of negotiation in Ambient Intelligence (AmI), a domain with a special emphasis iii on computational efficiency due to the limited capability of AmI devices. In the second part of the thesis we propose several negotiation models for agent-based negotiation teams. A negotiation team is a group of individuals that acts together as single negotiation party due to its common interests in the negotiation at hand.The complexity of negotiation teams resides in the fact that despite having common interests, intra-team conflict is also present. As far as we are concerned, the topic of negotiation teams in MAS is introduced with this thesis.iv ResumenLos sistemas multi-agente (SMA) son sistemas distribuidos donde entidades autó-nomas llamadas agentes, ya sean humanos o software, persiguen sus propios objetivos. El paradigma de SMA ha sido propuesto como la aproximación de modelo apropiada para aplicaciones como el comercio electrónico, los sistemas multi-robot, aplicaciones de seguridad, etc. En la comunidad de SMA, la visión de sistemas multi-agente abiertos, donde agentes heterogéneos pueden entrar y salir del sistema dinámicamente, ha cobrado fuerza como paradigma de modelado debido a su relación conceptual con tecnolo...

show abstract

HoCaMA: Home Care Hybrid Multiagent Architecture

Cited by 11 publications

References 20 publications

Dynamic Homecare Service Provisioning: A Field Test and Its Results

Dynamic Homecare Service Provisioning: A Field Test and Its Results

Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions

Complex negotiations in multi-agent systems.

Contact Info

Product

Resources

About