Building Ethically Bounded AI

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI's freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar

CP-nets: From Theory to Practice

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data

A systems biology approach to multi-scale modelling and analysis of planar cell polarity in drosophila melanogaster wing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Systems biology aims to describe and understand biology at a global scale where biological systems function as a result of complex mechanisms that happen at several scales. Modelling and simulation are computational tools that are invaluable for description, understanding and prediction these mechanisms in a quantitative and integrative way. Thus multi-scale methods that couple the design, simulation and analysis of models spanning several spatial and temporal scales is becoming a new emerging focus of systems biology. This thesis uses an exemplar – Planar cell polarity (PCP) signalling – to illustrate a generic approach to model biological systems at different spatial scales, using the new concept of Hierarchically Coloured Petri Nets (HCPN). PCP signalling refers to the coordinated polarisation of cells within the plane of various epithelial tissues to generate sub-cellular asymmetry along an axis orthogonal to their apical-basal axes. This polarisation is required for many developmental events in both vertebrates and non-vertebrates. Defects in PCP in vertebrates are responsible for developmental abnormalities in multiple tissues including the neural tube, the kidney and the inner ear. In Drosophila wing, PCP is seen in the parallel orientation of hairs that protrude from each of the approximately 30,000 epithelial cells to robustly point toward the wing tip. This work applies HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) in Drosophila wing. HCPN facilitate the construction of mathematically tractable, compact and parameterised large-scale models. Different levels of abstraction that can be used in order to simplify such a complex system are first illustrated. The PCP system is first represented at an abstract level without modelling details of the cell. Each cell is then sub-divided into seven virtual compartments with adjacent cells being coupled via the formation of intercellular complexes. A more detailed model is later developed, describing the intra- and inter-cellular signalling mechanisms involved in PCP signalling. The initial model is for a wild-type organism, and then a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP, are constructed. Among them, the largest model consists of 800 cells which when unfolded yields 164,000 places (each of which is described by an ordinary differential equation). This thesis illustrates the power and validity of the approach by showing how the models can be easily adapted to describe well-documented genetic mutations in the Drosophila wing using the proposed approach including clustering and model checking over time series of primary and secondary data, which can be employed to analyse and check such multi-scale models similar to the case of PCP. The HCPN models support the interpretation of biological observations reported in literature and are able to make sensible predictions. As HCPN model multi-scale systems in a compact, parameterised and scalable way, this modelling approach can be applied to other large-scale or multi-scale systems.This study was funded by Brunel University

Learning Conditional Preference Networks from Optimal Choices

Conditional preference networks (CP-nets) model user preferences over objects described in terms of values assigned to discrete features, where the preference for one feature may depend on the values of other features. Most existing algorithms for learning CP-nets from the user\u27s choices assume that the user chooses between pairs of objects. However, many real-world applications involve the the user choosing from all combinatorial possibilities or a very large subset. We introduce a CP-net learning algorithm for the latter type of choice, and study its properties formally and empirically

A satisficing bi-directional model transformation engine using mixed integer linear programming

The use of model transformation in software engineering has increased significantly during the past decade, with the ability to rapidly transform models and ensure consistency between those models being a key property of Model Driven Architecture. However, these approaches can be applied to a wide variety of different model types and some of these models and associated transformations require different semantics than those popularised by current model transformation tools. Specifically, current relational model transformation languages typically prioritise matching relation patterns in the source model over creating a target model that is compliant with its meta-model. In this paper we describe a relational model transformation engine implemented as a series of Mixed Integer Linear Programs (MILP). This engine has a key novel feature; it prioritises target model compliance with its meta-model by considering multiple interpretations of applying the transformation specification in order to ensure a correct target model is generated. In this paper the MILP transformation engine and the representations it uses are described, followed by the results of applying it to examples of varying complexity. © JOT 2011

Graphical preference representation under a possibilistic framework

La modélisation structurée de préférences, fondée sur les notions d'indépendance préférentielle, a un potentiel énorme pour fournir des approches efficaces pour la représentation et le raisonnement sur les préférences des décideurs dans les applications de la vie réelle. Cette thèse soulève la question de la représentation des préférences par une structure graphique. Nous proposons une nouvelle lecture de réseaux possibilistes, que nous appelons p-pref nets, où les degrés de possibilité représentent des degrés de satisfaction. L'approche utilise des poids de possibilité non instanciés (appelés poids symboliques), pour définir les tables de préférences conditionnelles. Ces tables donnent naissance à des vecteurs de poids symboliques qui codent les préférences qui sont satisfaites et celles qui sont violées dans un contexte donné. Nous nous concentrons ensuite sur les aspects théoriques de la manipulation de ces vecteurs. En effet, la comparaison de ces vecteurs peut s'appuyer sur différentes méthodes: celles induites par la règle de chaînage basée sur le produit ou celle basée sur le minimum que sous-tend le réseau possibiliste, les raffinements du minimum le discrimin, ou leximin, ainsi que l'ordre Pareto, et le Pareto symétrique qui le raffine. Nous prouvons que la comparaison par produit correspond exactement au celle du Pareto symétrique et nous nous concentrons sur les avantages de ce dernier par rapport aux autres méthodes. En outre, nous montrons que l'ordre du produit est consistant avec celui obtenu en comparant des ensembles de préférences satisfaites des tables. L'image est complétée par la proposition des algorithmes d'optimisation et de dominance pour les p-pref nets. Dans ce travail, nous discutons divers outils graphiques pour la représentation des préférences. Nous nous focalisons en particulier sur les CP-nets car ils partagent la même structure graphique que les p-pref nets et sont basés sur la même nature de préférences. Nous prouvons que les ordres induits par les CP-nets ne peuvent pas contredire ceux des p-pref nets et nous avons fixé les contraintes nécessaires pour raffiner les ordres des p-pref nets afin de capturer les contraintes Ceteris Paribus des CP-nets. Cela indique que les CP-nets représentent potentiellement une sous-classe des p-pref nets avec des contraintes. Ensuite, nous fournissons une comparaison approfondie entre les différents modèles graphiques qualitatifs et quantitatifs, et les p-pref nets. Nous en déduisons que ces derniers peuvent être placés à mi- chemin entre les modèles qualitatifs et les modèles quantitatifs puisqu'ils ne nécessitent pas une instanciation complète des poids symboliques alors que des informations supplémentaires sur l'importance des poids peuvent être prises en compte. La dernière partie de ce travail est consacrée à l'extension du modèle proposé pour représenter les préférences de plusieurs agents. Dans un premier temps, nous proposons l'utilisation de réseaux possibilistes où les préférences sont de type tout ou rien et nous définissons le conditionnement dans le cas de distributions booléennes. Nous montrons par ailleurs que ces réseaux multi-agents ont une contrepartie logique utile pour vérifier la cohérence des agents. Nous expliquons les étapes principales pour transformer ces réseaux en format logique. Enfin, nous décrivons une extension pour représenter des préférences nuancées et fournissons des algorithmes pour les requêtes d'optimisation et de dominance.Structured modeling of preference statements, grounded in the notions of preferential independence, has tremendous potential to provide efficient approaches for modeling and reasoning about decision maker preferences in real-life applications. This thesis raises the question of representing preferences through a graphical structure. We propose a new reading of possibilistic networks, that we call p-pref nets, where possibility weights represent satisfaction degrees. The approach uses non-instantiated possibility weights, which we call symbolic weights, to define conditional preference tables. These conditional preference tables give birth to vectors of symbolic weights that reflect the preferences that are satisfied and those that are violated in a considered situation. We then focus on the theoretical aspects of handling of these vectors. Indeed, the comparison of such vectors may rely on different orderings: the ones induced by the product-based, or the minimum based chain rule underlying the possibilistic network, the discrimin, or leximin refinements of the minimum- based ordering, as well as Pareto ordering, and the symmetric Pareto ordering that refines it. We prove that the product-based comparison corresponds exactly to symmetric Pareto and we focus on its assets compared to the other ordering methods. Besides, we show that productbased ordering is consistent with the ordering obtained by comparing sets of satisfied preference tables. The picture is then completed by the proposition of algorithms for handling optimization and dominance queries. In this work we discuss various graphical tools for preference representation. We shed light particularly on CP-nets since they share the same graphical structure as p-pref nets and are based on the same preference statements. We prove that the CP-net orderings cannot contradict those of the p-pref nets and we found suitable additional constraints to refine p-pref net orderings in order to capture Ceteris Paribus constraints of CP-nets. This indicates that CP-nets potentially represent a subclass of p-pref nets with constraints. Finally, we provide an thorough comparison between the different qualitative and quantitative graphical models and p-pref nets. We deduce that the latter can be positioned halfway between qualitative and quantitative models since they do not need a full instantiation of the symbolic weights while additional information about the relative strengths of these weights can be taken into account. The last part of this work is dedicated to extent the proposed model to represent multiple agents preferences. As a first step, we propose the use of possibilistic networks for representing all or nothing multiple agents preferences and define conditioning in the case of Boolean possibilities. These multiple agents networks have a logical counterpart helpful for checking agents consistency. We explain the main steps for transforming multiple agents networks into logical format. Finally, we outline an extension with priority levels of these networks and provide algorithms for handling optimization and dominance queries