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.

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.

The marketplace for definitions and theories of creativity is crowded indeed. No hard consensus exists on the elements of an all-embracing theory, or on what specific sub-processes and representations are required to support creativity, either in humans or in machines. Yet commonalities do exist across theories: a search for novelty and utility is implied by most theories, as is the notion that an innovation can be considered creative only if it is not too novel, and can be adequately grounded in the familiar and the understandable. Computational creativity (CC) is the pursuit of creative behavior in machines, and seeks inspiration from both AI and from human psychology. As a practical engineering endeavor, CC can afford to adopt a cafeteria approach to theories of creativity, taking what it needs from different theories and frameworks. In this paper we present a vision for CC research in the age of the Web, in which CC is provided on tap, via a suite of Web services, to any third-party application that needs it. We argue that this notion of Creativity as a Service – which is already a popular business model for human organizations – will allow CC researchers and developers to build ad-hoc mash-ups of whatever processes and representations are most suited to a given application. By offering CC as a centralized service, we can collect statistics on the most useful mash-ups, and therein obtain a new empirical basis for theorizing about creativity in humans and in machines.

Ironic utterances promise an expected meaning that never arrives, and deliver instead a meaning that exposes the failure of our expectations. Though they can appear contextually inappropriate, ironic statements succeed when they subvert their context of use, so it is the context rather than the utterance that is shown to be incongruous. Every ironic statement thus poses two related questions: the first, “what is unexpected about my meaning?” helps us answer the second, “what is unexpected about my context of use?”. Like metaphor, irony is not overtly marked, and relies instead on a listener’s understanding of stereotypical norms to unpack its true meaning. In this paper we consider how irony relies upon and subverts our stereotypical knowledge of a domain, and show how this knowledge can be exploited to both recognize and generate ironic similes for a topic.

Introspection is a question-led process in which one builds on what one already knows to explore what is possible and plausible. In creative introspection, whether in art or in science, framing the right question is as important as finding the right answer. Presupposition-laden questions are themselves a source of knowledge, and in this paper we show how widely-held beliefs about the world can be dynamically acquired by harvesting such questions from the Web. We show how metaphorical reasoning can be modeled as an introspective process, one that builds on questions harvested from the Web to pose further speculative questions and queries. Metaphor is much more than a knowledge-hungry rhetorical device: it is a conceptual lever that allows a system to extend its model of the world.

The difference between comedians and their audience is a matter not of kind, but of degree, a difference that is reflected in the vocational emphasis they place on humor. Researchers in the field of computational creativity find themselves in a similar situation. As a subdiscipline of artificial intelligence, computational creativity explores theories and practices that give rise to a phenomenon, creativity, that all intelligent systems, human or machine, can legitimately lay claim to. Who is to say that a given AI system is not creative, insofar as it solves nontrivial problems or generates useful outputs that are not hard wired into its programming? As with comedians' being funny, the difference between studying computational creativity and studying artificial intelligence is one of emphasis rather than one of kind: the field of computational creativity, as typified by a long-running series of workshops at AIrelated conferences, places a vocational emphasis on creativity and attempts to draw together the commonalities of what