Geometry Friendsis a collaborative platform game featuring two agents, a circle and a rectangle, navigating through physics-driven levels to collect diamonds. Each level presents challenges in the form of obstacles and diamonds arranged in different configurations that forces the agents to cooperate. In this article, we describe the Universidad Complutense de Madrid (UCM) agents, that participated in the 2023Geometry FriendsAI Competition with excellent results, especially in the collaborative track. Our approach combines level analysis, motion simulation, abstract planning, scripted actions, reinforcement learning, and different types of agent interaction. The contributions are versatile and potentially applicable to other collaborative platform games.

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Recent findings suggest that a single explainer may not meet the diverse needs of multiple users in an AI system; indeed, even individual users may require multiple explanations. This highlights the necessity for a “multi-shot” approach, employing a combination of explainers to form what we introduce as an “explanation strategy”. Tailored to a specific user or a user group, an “explanation experience” describes interactions with personalised strategies designed to enhance their AI decision-making processes. The iSee platform is designed for the intelligent sharing and reuse of explanation experiences, using Case-based Reasoning to advance best practices in XAI. The platform provides tools that enable AI system designers, i.e. design users, to design and iteratively revise the most suitable explanation strategy for their AI system to satisfy end-user needs. All knowledge generated within the iSee platform is formalised by the iSee ontology for interoperability. We use a summative mixed methods study protocol to evaluate the usability and utility of the iSEE platform with six design users across varying levels of AI and XAI expertise. Our findings confirm that the iSee platform effectively generalises across applications and its potential to promote the adoption of XAI best practices.

Explainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task. This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanation experiences are generated using Case-Based Reasoning. An explanation experience includes the proposed solution to a particular explainability problem and its corresponding evaluation, provided by the end user. The ultimate goal is to provide an open catalog of explanation experiences that can be transferred to other scenarios where trustworthy AI is required.

The Becalm project is an open and low-cost solution for the remote monitoring of respiratory support therapies like the ones used in COVID-19 patients. Becalm combines a decision-making system based on Case-Based Reasoning with a low-cost, non-invasive mask that enables the remote monitoring, detection, and explanation of risk situations for respiratory patients. This paper first describes the mask and the sensors that allow remote monitoring. Then, it describes the intelligent decision-making system that detects anomalies and raises early warnings. This detection is based on the comparison of cases that represent patients using a set of static variables plus the dynamic vector of the patient time series from sensors. Finally, personalized visual reports are created to explain the causes of the warning, data patterns, and patient context to the healthcare professional. To evaluate the case-based early-warning system, we use a synthetic data generator that simulates patients' clinical evolution from the physiological features and factors described in healthcare literature. This generation process has been verified with a real dataset and allows the validation of the reasoning system with noisy and incomplete data, threshold values, and life/death situations. The evaluation demonstrates promising results and good accuracy (0.91) for the proposed low-cost solution to monitor respiratory patients.

Lower back Pain (LBP) is pathological and occurs in about 80% of the population at least once in their life. Physiotherapists personalise manual treatments to heal or relieve pain according to the patient characteristics. The contribution of this methodological paper is the description and evaluation of the configuration software associated to a therapy machine that executes back segment mobilisations. The configuration software uses Case-Based Reasoning (CBR), a successful Machine Learning technique, based on mimicking the human decision making process by reusing previously applied configuration episodes on similar individuals. This paper demonstrates its feasibility and cost-effectiveness for the configuration of treatments as it reuses expert knowledge and maximises effectiveness by taking into account the patient’s personal medical record and similar patterns among different patients. Having a baseline of 31% success rate using a standard solution based on interpolation, the CBR engine can achieve, on average, up to 70% success rate when proposing a machine configuration to the physiotherapist. Regarding clinical results, we run a longitudinal observational study that achieves an average improvement of 31.63% using the pain Visual Analogue Scale (VAS), a 7% according to the Oswestry Disability Index (ODI), and 13% in the 36-Item Short Form Health Survey (SF-36).

Social systems by their definition encourage interaction between users and both on-line content and other users thus generating new sources of knowledge that is valuable for recommender systems. In this paper we deal with the situation of having a recommender system where, even if a social structure implicitly exist, its users are not explicitly connected through a social network. We describe SocialFan, a domain independent tool that allows defining and integrating the social network infrastructure to capture and use the social knowledge into an existing recommender system.

Case-based reasoning addresses new problems by remembering and adapting solutions previously used to solve similar problems. Pulled by the increasing number of applications and pushed by a growing interest in memory intensive techniques, research on case-based reasoning appears to be gaining momentum. In this article, we briefly summarize recent developments in research on case-based reasoning based partly on the recent Twenty Fourth International Conference on Case-Based Reasoning.

Classical recommender systems focus on recom- mending the most relevant items to users. An active area of research proposes to complete the recommendation process by considering additional contextual information, such as time, location, budget, weather or social position. Researchers and practitioners in different domains have already recognized the great impact of contextual information in decision-making pro- cesses. In this paper, we focus on recommenders for tourism and leisure activities where contextual information plays a central role to modify the initial user preferences. We present Madrid Live, a context-aware recommender system (CARS) to recommend leisure activities in Madrid. In Madrid Live, users state their own restrictions and preferences to their plans. The system recommends the set of activities that satisfies these preferences together with the contextual knowledge. The main contributions of our approach are the contextual recommendation and the system explanation interface that allows the user to understand the recommendation process.

Nowadays, recommender systems are useful tools to filter items and information for users. There is a huge diversity of approaches to create customized recommendations. Because of this, a developer needs to know the features of these approaches to select which one is the best approach in a specific domain. In this paper, we explain the first step in the design of our intelligent framework to create recommender systems. This first step is called RecOnto: an ontology to model recommender system as a collection of components related between them. This ontology defines and classifies all components that compound a recommender system. Moreover, depending on the information used by the recommender system, it can filter the components used by the system. In addition, this ontology can be extended to add more components or apply this model in other domains. Finally, we explain an example about how to apply RecOnto to model CoCARE, a real context-aware recommender system in the health domain.

Social recommender systems exploit the social knowledge available in social networks to provide accurate recommendations. However, their instantiation is not straightforward due to its complexity. To alleviate this development complexity, we propose a methodology based on templates that conceptualize the behavior of such applications and can be reused to create several social recommender applications in social networks. This development methodology comprises not only templates but also a generic architecture named ARISE and a collection of software components that provide the required functionality. We prove that our social templates speed up and facilitate the development process, and demonstrate the viability of our generic architecture in two different case studies.

In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.

The goal of this paper is to show a movie recommender system for groups of people, integrated in the social network Face book through an application called Happy movie. This application tries to mitigate certain limitations in existing group recommender systems, like obtaining the users profile or offering trading methods for users in order to reach a final agreement. The method used to make the group recommendation is based on three important features: personality, social trust and memory of past recommendations. This way we simulate in a more realistic way the argumentation process followed by groups of people when deciding a joint activity.

In this paper we describe some new ideas to improve recommendations to groups of people. Our approach maximizes the global satisfaction for the group taking into account people personality and the social relationships among people in the group. We present some results with two cases of study based on the movie recommendation domain with heterogeneous groups. The first case study uses synthetically generated groups of people to test how the group composition affects the accuracy of the recommendation. Our second case study uses real users and groups where the topology of the groups is based on a social network. This second case of study with real users confirms the wide conclusions of the preliminary experiment with synthetic data, which allows us to conclude that it is possible to realize trustworthy experiments with synthetic data.

jCOLIBRI is currently a reference platform in the CBR community for building CBR systems that includes facilities to design different types of CBR applications \cite{ICCBR05CBRT,jscp07BuildingCBRsystems,AI06OntBasedCBR}. In this paper we focus in some recently included tools that allow the improvement of performance of previously designed applications. These optimization tools mainly facilitate to adjust features on large case bases like clustering and noise reduction techniques, and to adjust processes like refine similarity metrics through case base visualization, parallelization of retrieval or distribution of the case base and reasoning thought different agents. We present the tools and exemplify how to use them in a real scenario. We have developed an experiment for the automatic classification of a textual case base made of 1500 academic journals belonging to 20 different areas.

In this article we introduce a novel method of making recommendations to groups based on existing techniques of collaborative filtering and taking into account the group personality composition. We have tested our method in the movie recommendation domain and we have experimentally evaluated its behavior under heterogeneous groups according to the group personality composition.

Artificial intelligence in games is typically used for creating player's opponents. Manual editing of intelligent behaviors for nonplayer characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviors typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical structures like hierarchical state machines, behavior trees (BTs), or hierarchical task networks, allows working on different abstraction levels reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden of completely specifying each behavior. Our approach applies case-based reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as BTs. In this paper, we focus on dynamic retrieval and selection of behaviors taking into account the world state and the underlying goals. The global behavior of the NPC is dynamically built at runtime querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from first-person shooter (FPS) games.

The goals, technologies and problems related to the Semantic Web are well known for research purposes. Due to its extent, the inclusion of the concepts that concern to the Semantic Web in Computer Science courses is not easy. In this paper we detail our experience on a project-oriented approach to learn and to put into practice the main problems, concepts and technologies related to the Semantic Web. The project domain focuses on semantic mark up and retrieval of pictures, and the comparison between syntactical and semantic retrieval methods.

Our goal is to support system developers in rapid prototyping recommender systems using Case-Based Reasoning (CBR) techniques. In this paper we describe how jCOLIBRI can serve to that goal. jCOLIBRI is an object-oriented framework in Java for building CBR systems that greatly benefits from the reuse of previously developed CBR systems.

Case Based Reasoning (CBR) systems reason by similarity between current unsolved problems and past solved problems. In such systems interaction between users and the reasoning module is very important to identify the specific aspects of the query problem. In this paper we describe a textual interface module included into JCOLIBRI, a Java framework to design CBR systems. To be able to "understand" the query, the module processes the text using information extraction, analysis and reasoning techniques, based on external resources like domain ontologies and other linguistic resources such as Wordnet.

Case-based reasoning (CBR) is a paradigm for combining problem solving and learning that has become one of the most successful applied subfields of AI in recent years. Now that CBR has become a mature and established technology two necessities have become critical: the availability of tools to build CBR systems, and the accumulated practical experience of applying CBR techniques to real-world problems. In this paper we are presenting jcolibri, an object-oriented framework in Java for building CBR systems, that greatly benefits from the reuse of previously developed CBR systems.

In this article we describe a domain-independent model to manage the conversation strategies between users and computers. We apply the model in COBBER, a Conversational case-based reasoning (CCBR) framework based on reusable ontologies. The overall goal of the model is to support users following an affective approach in order to keep them in the adequate mood to interact with the computer. We describe the main tasks of the framework and the methodology to instantiate the conversation strategies for a specific domain. We design the dynamic behavior of conversation strategies with causal loops from system dynamics theory. We adapt this theoretical framework to work with the CCBR approach.