Rules offer a convenient means of limiting the operational scope of our AI programs so as to not transgress predictable moral boundaries. Yet the imposition of an operational morality based on mere rules will not turn our machines into moral agents, just the unthinking tools of moral designers. If we are to imbue our machines with a profound functional morality, we must first gift them with a moral imagination, for empathic morality — where one agent treats another as it would want to be treated itself — requires an ability to project oneself into the realms of the counterfactual. In this paper we thus explore the role of the moral imagination in generating new and inspiring stories. The creation of novel tales with a built-in moral requires that an artificial system possess the ability to guess at the morality of characters and their actions in novel settings and events. Our moralizing tale-spinner — which generates Aesop-style tales about human-like animals with identifiable human qualities — also faces another challenge: it must render these tales as micro-texts that can be distributed as tweets. As we shall also use metaphor to lend elasticity to our moral conceptions, these short stories, rich in animal metaphors, will comprise part of the daily output of the @MetaphorMagnet Twitterbot.

Recommender systems learn about our preferences to make targeted suggestions. In this paper we outline a novel game-with-a-purpose designed to infer preferences at scale as a side-effect of gameplay. We evaluate the utility of this data in a recommendation context as part of a small live-user trial.

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.

The program also included six keynote presentations, a funding panel, a community panel, and multiple breakout sessions. The keynote presentations, given by speakers that have been working on AI for HRI for many years, focused on the larger intellectual picture of this subfield. Each speaker was asked to address, from his or her personal perspective, why HRI is an AI problem and how AI research can bring us closer to the reality of humans interacting with robots on everyday tasks. Speakers included Rodney Brooks (Rethink Robotics), Manuela Veloso (Carnegie Mellon University), Michael Goodrich (Brigham Young University), Benjamin Kuipers (University of Michigan), Maja Mataric (University of Southern California), and Brian Scassellati (Yale University).

We all share the same world, but are free to formulate and argue for our own interpretations of this shared reality. For different agents will grant differing degrees of importance to the same facts and norms. We cannot experiment on human cultures the way scientists experiment on cell cultures, but we can construct thought experiments that imagine the consequences of otherwise impossible changes. Successful thought experiments do not change the world, but change the way we see the world. This paper describes Gedanken-style reasoning in an AI system that allows a computer to understand, or at least speculate on, the surprising causal interactions between apparently unrelated concepts. This system ponders alternate worlds in which the amount of a conceptual ingredient [X] is increased or decreased, to see what unexpected and apparently incongruous effects might arise from this change. Our goal is to construct a creative generator of novel what-if scenarios that can be used in the generation of perspective-shaping stories, poems and jokes.

A set of constraints that cannot be simultaneously satisfied is over-constrained. Minimal relaxations and minimal explanations for over-constrained problems find many practical uses. For Boolean formulas, minimal relaxations of over-constrained problems are referred to as Minimal Correction Subsets (MCSes). MCSes find many applications, including the enumeration of MUSes. Existing approaches for computing MCSes either use a Maximum Satisfiability (MaxSAT) solver or iterative calls to a Boolean Satisfiability (SAT) solver. This paper shows that existing algorithms for MCS computation can be inefficient, and so inadequate, in certain practical settings. To address this problem, this paper develops a number of novel techniques for improving the performance of existing MCS computation algorithms. More importantly, the paper proposes a novel algorithm for computing MCSes. Both the techniques and the algorithm are evaluated empirically on representative problem instances, and are shown to yield the most efficient and robust solutions for MCS computation.

Minimal explanations of infeasibility find a wide range of uses. In the Boolean domain, these are referred to as Minimal Unsatisfiable Subsets (MUSes). In some settings, one needs to enumerate MUSes of a Boolean formula. Most often the goal is to enumerate all MUSes. In cases where this is computationally infeasible, an alternative is to enumerate some MUSes. This paper develops a novel approach for partial enumeration of MUSes, that complements existing alternatives. If the enumeration of all MUSes is viable, then existing alternatives represent the best option. However, for formulas where the enumeration of all MUSes is unrealistic, our approach provides a solution for enumerating some MUSes within a given time bound. The experimental results focus on formulas for which existing solutions are unable to enumerate MUSes, and shows that the new approach can in most cases enumerate a non-negligible number of MUSes within a given time bound.

Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficultyto keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase ordecrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimizethe player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.

Current approaches for word-sense disambiguation (WSD) try to relate the senses of the target words by optimizing a score for each sense in the context of all other words' senses. However, by scoring each sense separately, they often fail to optimize the relations between the resulting senses. We address this problem by proposing a HITS-inspired method that attempts to optimize the score for the entire sense combination rather than one-word-at-a-time. We also exploit word-sense disambiguation via topic-models, when retrieving senses from heterogeneous sense inventories. Although this entails the relaxation of several assumptions behind current WSD algorithms, we show that our proposed method E-WSD achieves better results than current state-of-the-art approaches, without the need for additional background knowledge.

Discussion boards are a form of social media which allow users to discuss topics and exchange information in a complex manner, in a number of different settings. As the popularity of such message boards has increased, communities of users have emerged, and several prominent types of social role have been identified, such as Question Answerer, Celebrity, Discussion Person and Topic Initiator. Recent studies have noted the structural similarity of the egocentric network of users assigned the same role by qualitative criteria. In this paper a methodology is developed with which to cluster together users with similar ego-centric network structures. This is achieved using a mixed membership formulation which allows for the fact that different groups of users may have characteristics in common. The method is then applied to data taken from boards.ie, a medium sized message boards website. Prominent clusters of users are identified and discussed, and illustrative examples of user behaviour provided. The type of interaction, both locally and globally, taking place within forums is examined.