CBR driven interactive explainable AI.

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requires employing a combination of these explainers. We refer to such combinations as explanation strategies. This paper introduces iSee - Intelligent Sharing of Explanation Experience, an interactive platform that facilitates the reuse of explanation strategies and promotes best practices in XAI by employing the Case-based Reasoning (CBR) paradigm. iSee uses an ontology-guided approach to effectively capture explanation requirements, while a behaviour tree-driven conversational chatbot captures user experiences of interacting with the explanations and provides feedback. In a case study, we illustrate the iSee CBR system capabilities by formalising a realworld radiograph fracture detection system and demonstrating how each interactive tools facilitate the CBR processes

Incorporating temporal-bounded CBR techniques in real-time agents

Nowadays, MAS paradigm tries to move Computation to a new level of abstraction: Computation as interaction, where large complex systems are seen in terms of the services they offer, and consequently in terms of the entities or agents providing or consuming services. However, MAS technology is found to be lacking in some critical environments as real-time environments. An interaction-based vision of a real-time system involves the purchase of a responsibility by any entity or agent for the accomplishment of a required service under possibly hard or soft temporal conditions. This vision notably increases the complexity of these kinds of systems. The main problem in the architecture development of agents in real-time environments is with the deliberation process where it is difficult to integrate complex bounded deliberative processes for decision-making in a simple and efficient way. According to this, this work presents a temporal-bounded deliberative case-based behaviour as an anytime solution. More specifically, the work proposes a new temporal-bounded CBR algorithm which facilitates deliberative processes for agents in real-time environments, which need both real-time and deliberative capabilities. The paper presents too an application example for the automated management simulation of internal and external mail in a department plant. This example has allowed to evaluate the proposal investigating the performance of the system and the temporal-bounded deliberative case-based behaviour. 2010 Elsevier Ltd. All rights reserved.This work is supported by TIN2006-14630-C03-01 projects of the Spanish government, GVPRE/2008/070 project, FEDER funds and CONSOLIDER-INGENIO 2010 under Grant CSD2007-00022.Navarro Llácer, M.; Heras Barberá, SM.; Julian Inglada, VJ.; Botti Navarro, VJ. (2011). Incorporating temporal-bounded CBR techniques in real-time agents. Expert Systems with Applications. 38(3):2783-2796. https://doi.org/10.1016/j.eswa.2010.08.070S2783279638

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Towards real-time agreements

In this paper, we deal with the problem of real-time coordination with the more general approach of reaching real-time agreements in MAS. Concretely, this work proposes a real-time argumentation framework in an attempt to provide agents with the ability of engaging in argumentative dialogues and come with a solution for their underlying agreement process within a bounded period of time. The framework has been implemented and evaluated in the domain of a customer support application. Concretely, we consider a society of agents that act on behalf of a group of technicians that must solve problems in a Technology Management Centre (TMC) within a bounded time. This centre controls every process implicated in the provision of technological and customer support services to private or public organisations by means of a call centre. The contract signed between the TCM and the customer establishes penalties if the specified time is exceeded. 2012 Elsevier Ltd. All rights reserved.This work is supported by the Spanish Government grants TIN2009-13839-C03-01 [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PRO-METEO 2008/051].Navarro Llácer, M.; Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2013). Towards real-time agreements. Expert Systems with Applications. 40(10):3906-3917. https://doi.org/10.1016/j.eswa.2012.12.087S39063917401

Doctor of Philosophy

dissertationIn the United States, public universities must negotiate public responsibility with market interests, and are often under suspicion of being businesslike and detached from local community issues and concerns. Campus-community partnerships are gaining traction as a preferable way for public universities to bridge campus and community concerns. This dissertation is a qualitative case study of UPartner (UP), an organization that creates campus-community partnerships between a large public university and a community system identified by that university through a statistical analysis of zip codes that indicated underrepresentation at the university. In this dissertation, I explain my methodological perspective as an engaged advisor. Through in-depth interviews, participant observation, and historical research, I engaged with UP to understand how participants characterized their activities and strategized ways to change the university system. Using structuration theory as a framework, I explain how UP participants structure their activities and characterize the systems of campus and community. I discuss several discursive patterns and practices including Connection, Hopeland, Confusion, and Not Service/Outreach. I also discuss these patterns in light of their enabling and constraining qualities, and the extent to which they echo larger discourses concerning democracy and the market. I give particular focus to the activity of partnership, which is structured as Reciprocity, Sustainability, and Difficulty. Finally, I extend structurating activity theory's notion of contradictions to discuss several contradictions that UP participants encounter when trying to change the university system, including Deficit Discourses, The Marginalization of Community Based Research, and The Containment of UP. I explain each contradiction, and then show how UP participants attempt to overcome the contradiction through desired new discursive patterns

Case-based argumentation infrastructure for agent societies

In this work, we propose an infrastructure to develop and execute argumentative agents in an open multi-agent system. This infrastructure offers the necessary components to develop agents with argumentation capabilities, including the communication skills and the argumentation protocol, and it offers support for agent societies and their agents' social context. The main advantage of having this infrastructure is that it is possible to create agents with argumentation capabilities to resolve a specified problem. In the argumentation dialogue the agents try to reach an agreement about the best solution to apply for each proposed problem. The proposed infrastructure has been validated with a real example and it has been evaluated obtaining, with argumentation strategies, better performance than other reasoning approaches that do not include argumentation.Jordán Prunera, JM. (2011). Case-based argumentation infrastructure for agent societies. http://hdl.handle.net/10251/15362Archivo delegad

Sampling with Confidence: Using k-NN Confidence Measures in Active Learning

Active learning is a process through which classifiers can be built from collections of unlabelled examples through the cooperation of a human oracle who can label a small number of examples selected as most informative. Typically the most informative examples are selected through uncertainty sampling based on classification scores. However, previous work has shown that, contrary to expectations, there is not a direct relationship between classification scores and classification confidence. Fortunately, there exists a collection of particularly effective techniques for building measures of classification confidence from the similarity information generated by k-NN classifiers. This paper investigates using these confidence measures in a new active learning sampling selection strategy, and shows how the performance of this strategy is better than one based on uncertainty sampling using classification scores

IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process