We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.

Automatic facial expression recognition plays an important role in agent-based interface development and datadriven animation. This paper presents an intelligent facial action and emotion recognition system for a humanoid robot. Motivated by the Facial Action Coding System, this research focuses on the recognition of seven basic emotions and 18 Action Units (AU). Since effective facial representations of original face images are vital for automatic facial emotion recognition, this research implements a novel shape and appearance feature extraction method, which integrates an Independent Active Appearance Model (AAM) with a rotation-invariant feature point detector, BRISK (Binary Robust Invariant Scalable Keypoints). In comparison to AAM with a traditional inverse compositional fitting, our model with BRISK fitting is with less computational cost and is capable of dealing with feature extraction from images of faces with rotations and scaling differences without prior training required. Subsequently shape and appearancebased neural network AU analyzers are used to respectively detect 18 AUs. Emotions are then decoded from the derived AUs using a neural network emotion recognizer. The system is integrated with a modern humanoid robot platform. Evaluation results indicate its high accuracy for AU and emotion recognition. It is also among the top performers on the extended Cohn-Kanade (CK+) database in comparison to other existing state-of-the-art applications.

Metaphor is important in all sorts of mundane discourse: ordinary conversation, news articles, popular novels, advertisements, etc. Issues of prime human interest – such as relationships, money, disease, states of mind, passage of time – are often most economically and understandably conveyed throu gh metaphor. This ubiquity of metaphor presents a challenge to how Artificial Intelligence (AI) systems not only understand inter-human discourse (e.g. newspaper articles), but also produce more natural-seeming language; however, most AI research on metaphor has been about its understanding rather than its generation. To redress the balance, we directly tackle the role of AI systems in communication, uniquely combining this with corpus-based results to guide output toward more natural forms of expression.

We report work on adding an improvisational AI actor and 3D emotional animation to an existing edrama program, a system for dramatic improvisation in simple virtual scenarios. The improvisational AI actor has an affect-detection component, aimed at detecting affective aspects of human-controlled characters' textual input. It also makes an appropriate response to stimulate the improvisation based on this affective understanding. A distinctive feature of our work is a focus on the metaphorical ways in which affect is conveyed. Moreover, we have also introduced how the detected affective states activate the animation engine to produce emotional gestures for human-controlled characters. Finally, we report user testing conducted for the AI actor. Our work contributes to the conference themes on affective user interfaces, natural language processing and emotionally believable gesture generation.

This paper discusses the representation of propositional attitudes (beliefs, etc.) in connectionist systems that do not implement symbolic representations. One prominent way of symbolically representing attitudes is through meta-representational schemes. These have representational expressions that themselves refer to representational expressions. Meta-representation is one of the most expressively powerful symbolic approaches for attitude representation. Therefore: could non-implementational connectionist systems use an analogous approach? Unfortunately, it is not straightforward to devise a plausible analogy. The paper looks at three main possibilities: (i) the representational activation patterns of the non-implementational connectionist system refer to the system's own activation patterns; (ii) the activation patterns refer to formal symbolic expressions; and (iii) the activation patterns refer to natural-language expressions. These approaches, which are not claimed to be exhaustive, concentrate respectively on the following facts about symbolic meta-representation schemes: (1) they typically refer to their own representational expressions; (2) the schemes typically refer to formal symbolic expressions; although (3) some of the schemes refer to natural language expressions. The article briefly argues that possibility (iii) avoids some of the problems of (i) and (ii). There are also two independent, non-connectionism-derived reasons for considering (iii). One is that it is strongly related to a prevalent commonsense metaphor of beliefs and so on as internal, natural language utterances. (The other is to do with the heightened difficulty of handling vague quantification within propositional attitude contexts, but is discussed elsewhere and not in the present paper.) The paper as a whole highlights the point that even a non-implementational connectionist system must be able to think about complex symbolic constructs such as logic expressions and natural language phrases, even though it does not think with them.

Winner-take-all (WTA) networks frequently appear in neural network models. They are primarily used for decision making and selection. As an alternative to the conventional activation-based winner-take-all mechanisms (AWTA), we present a time-based temporal-winner-take-all mechanism with O(n) space complexity and roughly O(log n) time complexity. The mechanism exploits systematic and stochastic differences between time delays within different units and connections. The TWTA and the AWTA networks are shown to be logically equivalent, but the TWTA mechanism may be more suitable than the latter for various selection tasks, especially the selection of an arbitrary unit from a set (e.g., as in unit recruitment). TWTA avoids various problems with conventional WTA, notably the difficulty of making it converge rapidly over a large range of conditions. Here we report a probabilistic analysis of the TWTA mechanism along with experimental data obtained from numerous massively parallel simulations of the TWTA mechanism on the connection machine.

Evidence from real discourse suggests that beliefs and other mental states (propositional attitudes) are often viewed by speakers and other agents in a metaphorical way. Typical metaphors are MIND‐AS‐CONTAINER—the view of the mind as a container, with thoughts being physical objects inside it—or IDEAS‐AS‐INTERNAL‐UTTERANCES—the view of thoughts as natural language utterances inside an agent's head. It is therefore necessary for AI systems for mental‐state representation/reasoning to reason within such views. This approach contrasts with the highly abstract logical stance adopted in most propositional attitude research. A formal representation scheme based on the various metaphors has been partially developed. In this paper, it is mainly the MIND‐AS‐CONTAINER segment of the formal representation scheme that is detailed. Inference processes operating over the scheme are also presented. The crucial distinguishing feature of the representation scheme is that the description of mental states is directly based on physical predicates, objects and so on, as opposed to abstract, tailor‐made, mental ones. That is, the representation scheme is itself explicitly metaphor‐imbued.

Research programs must often divide issues into manageable sub-issues. The assumption is that an approach developed to cope with a sub-issue can later be integrated into an approach to the whole issue—possibly after some tinkering with the sub-approach, but without affecting its fundamental features. However, the present paper examines a case where an AI issue has been divided in a way that is, apparently, harmless and natural, but is, actually, fundamentally out of tune with the realities of the issue. As a result, some approaches developed for a certain sub-issue cannot be extended to a total approach without fundamental modification. The issue in question is that of representing and reasoning about people's beliefs, hopes, intentions and other ‘propositional attitudes’, and/or interpreting natural language sentences that report propositional attitudes. Researchers have, quite understandably, de-emphasized the problem of dealing in detail with nested attitudes (e.g., hopes about beliefs, beliefs about intentions about beliefs), in favour of concentrating on the sub-issue of non-nested attitudes. Unfortunately, a wide variety of approaches to attitudes are prone to a deep but somewhat subtle problem when they are applied to nested attitudes. This problem can be very roughly described as an AI system's unwitting imputation of its own arcane ‘theory’ of propositional attitudes to other agents. The details of this phenomenon have been published elsewhere by the author: the present paper therefore merely sketches it, and concentrates instead on the methodological lessons to be drawn, both for propositional attitude research and, more tentatively, for AI in general. The paper also summarizes an argument (presented more completely elsewhere) for an approach to attitude representation based in part on metaphors of mind that are commonly used by people. This proposed new research direction should ultimately coax propositional attitude research out of the logical armchair and into the psychological laboratory.

The design of languages for programming complex discrete-event simulations, with a conventional global notion of simulation time, is considered.An example language is described in which the main objects dealt with are concrete manifestations of phases of activity conceptually underlying a simulation and in which programs have an extremely nonsequential mode of notional operation.A program in the language very directly specifies a certain type of formal simulation system which generates a unique maximal simulation history.This history is a sequence of time instants, each associated with the partially ordered set of events considered to occur at that instant.In contrast to the case of programs in existing discrete-event simulation languages, (1) the event orders achievable at instants can be partial and are not restricted in any way, and (2) phase objects are central and explicit in a program, being the main units describing overall structure, data, and control.Variants of the language could have uses other than simulation.