Papers by Roberto Confalonieri
This paper motivates the use of computational argumentation for evaluating ‘concept blends ’ and ... more This paper motivates the use of computational argumentation for evaluating ‘concept blends ’ and other forms of combina-torial creativity. We exemplify our approach in the domain of computer icon design, where icons are understood as creative artefacts generated through concept blending. We present a semiotic system for representing icons, showing how they can be described in terms of interpretations and how they are related by sign patterns. The interpretation of a sign pat-tern conveys an intended meaning for an icon. This intended meaning is subjective, and depends on the way concept blend-ing for creating the icon is realised. We show how the in-tended meaning of icons can be discussed in an explicit and social argumentation process modeled as a dialogue game, and show examples of these following the style of Lakatos (1976). In this way, we are able to evaluate concept blends through an open-ended and dynamic discussion in which con-cept blends can be improved and the reasons be...
In this paper we describe our orchestration model for IRS-III. IRS-III is a framework and platfor... more In this paper we describe our orchestration model for IRS-III. IRS-III is a framework and platform for developing WSMO based semantic web services. Orchestration specifies how a complex web service calls subordinate web services. Our orchestration model is state-based: control and data flow are defined by and in states respectively; web services and goals are modeled as activities and their execution triggers state changes. The model is illustrated with a simple example.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Axiom weakening is a novel technique that allows for fine-grained repair of inconsistent ontologi... more Axiom weakening is a novel technique that allows for fine-grained repair of inconsistent ontologies. In a multi-agent setting, integrating ontologies corresponding to multiple agents may lead to inconsistencies. Such inconsistencies can be resolved after the integrated ontology has been built, or their generation can be prevented during ontology generation. We implement and compare these two approaches. First, we study how to repair an inconsistent ontology resulting from a voting-based aggregation of views of heterogeneous agents. Second, we prevent the generation of inconsistencies by letting the agents engage in a turn-based rational protocol about the axioms to be added to the integrated ontology. We instantiate the two approaches using real-world ontologies and compare them by measuring the levels of satisfaction of the agents w.r.t. the ontology obtained by the two procedures.
We introduce and discuss a knowledge-driven distillation approach to explaining black-box models ... more We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an approach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks.
This extended abstract overview the work presented in [1] where an extension of Trepan is propose... more This extended abstract overview the work presented in [1] where an extension of Trepan is proposed. Trepan is a seminal global explanation approach that extracts surrogate decision trees from black-box models. Trepan was extended to take into account explicit knowledge, modeled by means of ontologies, to extract human-understadable explanations. Trepan is a tree induction algorithm that recursively extracts decision trees from black-box classifiers [2]. The algorithm is model-agnostic, and it can be applied to explain any black-box classifier (e.g., Multi-Layer Perceptron, Random Forest). Trepan combines the learning of the decision tree with a trained machine learning classifier (the oracle). The proposed extension of the Trepan algorithm, called Trepan Reloaded, uses a modified information gain that, in the creation of split nodes, gives priority to features associated with more general concepts defined in a domain ontology. This was achieved by means of an information content mea...
Concept Invention, 2018
The goal of the COINVENT project was not only to develop a novel, computationally feasible, forma... more The goal of the COINVENT project was not only to develop a novel, computationally feasible, formal model of conceptual blending that was sufficiently precise for capturing the fundamental insights of Fauconnier and Turner's theory, but also to implement a creative computational system based on this novel formal model. In this chapter, we overview COBBLE, the concept invention system prototype that we developed, and we describe its enabling technologies. The technologies we adopted and developed draw from interdisciplinary fields from ontologies, analogical reasoning, logic programming and formal methods.
Conceptual blending is a powerful tool for computational creativity where, for example, the prope... more Conceptual blending is a powerful tool for computational creativity where, for example, the properties of two harmonic spaces may be combined in a consistent manner to produce a novel harmonic space. However, deciding about the importance of property features in the input spaces and evaluating the results of conceptual blending is a nontrivial task. In the specific case of musical harmony, defining the salient features of chord transitions and evaluating invented harmonic spaces requires deep musicological background knowledge. In this paper, we propose a creative tool that helps musicologists to evaluate and to enhance harmonic innovation. This tool allows a music expert to specify arguments over given transition properties. These arguments are then considered by the system when defining combinations of features in an idiom-blending process. A music expert can assess whether the new harmonic idiom makes musicological sense and re-adjust the arguments (selection of features) to expl...
Explainability in Artificial Intelligence has been revived as a topic of active research by the n... more Explainability in Artificial Intelligence has been revived as a topic of active research by the need of conveying safety and trust to users in the `how' and `why' of automated decision-making. Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how this influences the understandability of global explanations from the users' perspective. In this paper, we show how ontologies help the understandability of global post-hoc explanations, presented in the form of symbolic models. In particular, we build on Trepan, an algorithm that explains artificial neural networks by means of decision trees, and we extend it to include ontologies modeling domain knowledge in the process of generating explanations. We present the results of a user study that measures the understandability of decision trees using a syntactic complexity measure, and through time and accuracy of responses as well as reported user ...
Artificial Intelligence, 2021
Artificial Intelligence, 2018
We present a computational framework for conceptual blending, a concept invention method that is ... more We present a computational framework for conceptual blending, a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles, described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music.
Annals of Mathematics and Artificial Intelligence, 2016
Conceptual blending is a mental process that serves a variety cognitive purposes, including human... more Conceptual blending is a mental process that serves a variety cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinational creativity, a blend is constructed by taking the commonalities among the input mental spaces into account, to form a so-called Generic Space, and by projecting the non-common structure of the input spaces in a selective way to the novel blended space. Since input spaces for interesting blends are often initially incompatible, a generalisation step is needed before they can be blended. In this paper, we apply this idea to blend input spaces specified in the description logic EL ++ and propose an upward refinement operator for generalising EL ++ concepts. We show how the generalisation operator is translated to Answer Set Programming (ASP) in order to implement a search process that finds possible generalisations of input concepts. The generalisations obtained by the ASP process are used in a conceptual blending algorithm that generates and evaluates possible combinations of blends. We exemplify our approach in the domain of computer icons.
Engineering Applications of Artificial Intelligence, 2015
Multiuser museum interactives are computer systems installed in museums or galleries which allow ... more Multiuser museum interactives are computer systems installed in museums or galleries which allow several visitors to interact together with digital representations of artefacts and information from the museum's collection. In this paper, we describe WeCurate, a socio-technical system that supports cobrowsing across multiple devices and enables groups of users to collaboratively curate a collection of images, through negotiation, collective decision making and voting. The engineering of such a system is challenging since it requires to address several problems such as: distributed workflow control, collective decision making and multiuser synchronous interactions. The system uses a peer-to-peer Electronic Institution (EI) to manage and execute a distributed curation workflow and models community interactions into scenes, where users engage in different social activities. Social interactions are enacted by intelligent agents that interface the users participating in the curation workflow with the EI infrastructure. The multiagent system supports collective decision making, representing the actions of the users within the EI, where the agents advocate and support the desires of their users e.g. aggregating opinions for deciding which images are interesting enough to be discussed, and proposing interactions and resolutions between disagreeing group members. Throughout the paper, we describe the enabling technologies of WeCurate, the peer-to-peer EI infrastructure, the agent collective decision making capabilities and the multi-modal interface. We present a system evaluation based on data collected from cultural exhibitions in which WeCurate was used as supporting multiuser interactive.
We present a computational framework for chord invention based on a cognitive-theoretic perspecti... more We present a computational framework for chord invention based on a cognitive-theoretic perspective on conceptual blending. The framework builds on algebraic specifications, and solves two musicological problems. It automatically finds transitions between chord progressions of different keys or idioms, and it substitutes chords in a chord progression by other chords of a similar function , as a means to create novel variations. The approach is demonstrated with several examples where jazz cadences are invented by blending chords in cadences from earlier idioms, and where novel chord progressions are generated by inventing transition chords.
Annals of Mathematics and Artificial Intelligence
The cognitive-linguistic theory of conceptual blending was introduced by Fauconnier and Turner in... more The cognitive-linguistic theory of conceptual blending was introduced by Fauconnier and Turner in the late 90s to provide a descriptive model and foundational approach for the (almost uniquely) human ability to invent new concepts. Whilst blending is often described as 'fluid' and 'effortless' when ascribed to humans, it becomes a highly complex, multiparadigm problem in Artificial Intelligence. This paper aims at presenting a coherent computational narrative, focusing on how one may derive a formal reconstruction of conceptual blending from a deconstruction of the human ability of concept invention into some of its core components. It thus focuses on presenting the key facets that a computational framework for concept invention should possess. A central theme in our narrative is the notion of refinement, understood as ways of specialising or generalising concepts, an idea that can be seen as providing conceptual uniformity to a number of theoretical constructs as well as implementation efforts underlying computational versions of conceptual blending. Particular elements underlying our reconstruction effort include ontologies and ontology-based reasoning, image schema theory, spatio-temporal reasoning, abstract specification, social choice theory, and axiom pinpointing. We overview and analyse adopted solutions and then focus on open perspectives that address two core problems in computational approaches to conceptual blending: searching for the shared semantic structure between concepts-the socalled generic space in conceptual blending-and concept evaluation, i.e., to determine the value of newly found blends.
... In nonmonotonic reasoning, preferences can be implicit or explicit and they are used ... More... more ... In nonmonotonic reasoning, preferences can be implicit or explicit and they are used ... Moreover, several extensions have been proposed to deal with preferences [Delgrande et al ... knowledge representa-tion and reasoning tool and several proposals for dealing with incomplete ...
Visiting a museum with friends can be considered one of the preferred social experiences of peopl... more Visiting a museum with friends can be considered one of the preferred social experiences of people visiting a city. In such a social experience, users can share opinions with other users and together decide which cultural artefacts to see. Unfortunately, museums have been placed under financial pressure by the European economic crisis. Consequently, several museums in the UK reduced their opening hours [8] and, as such, physically visiting museums or seeing all the artefacts in a museum is becoming harder [9]. Internet may ...
In this paper we describe our orchestration model for IRS-III. IRS-III is a framework and platfor... more In this paper we describe our orchestration model for IRS-III. IRS-III is a framework and platform for developing WSMO based semantic web services. Orchestration specifies how a complex web service calls subordinate web services. Our orchestration model is state-based: control and data flow are defined by and in states respectively; web services and goals are modeled as activities and their execution triggers state changes. The model is illustrated with a simple example.
Communications in Computer and Information Science, 2012
The representation of preference queries to an uncertain data-base requires a framework capable o... more The representation of preference queries to an uncertain data-base requires a framework capable of dealing with preferences and uncertainty in a separate way. Possibilistic logic has shown to be a suitable setting to support different kinds of preference queries. In this paper, we propose a counterpart of the possibilistic logic-based preference query encoding within a possibilistic logic programming framework. Our approach is capable of dealing with the same interplay of preferences and uncertainty as in possibilistic logic.
Lecture Notes in Computer Science, 2011
Possibility theory offers a qualitative framework for modeling decision under uncertainty. In thi... more Possibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decision problems and computing optimal decisions in the sense of the possibilistic criteria. This is achieved by applying both a classic and a possibilistic ASP-based methodology in order to handle both a knowledge base pervaded with uncertainty and a prioritized preference base.
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Papers by Roberto Confalonieri