The big challenge for Artificial Intelligence is a better understanding of human nature. Our fundamental motivation is to understand the minds of modern people by uncovering mechanisms of the brain, genes, and body, and enhancing our health and cognitive talents with Artificial Intelligence technologies. This paper presents how we can quantify cognitive biases in the decision-making process and understand the evolutionary mechanisms using Ambient Intelligence and crowdsourced genetics technologies. We focus on prospect theory (proposed by Daniel Kahneman), which models how people choose between options involving gains or losses. People perceive losses to hurt more than gains feel good. This “loss aversion” is an important cognitive bias in decision-making. However, little is known about individual differences in loss aversion. We launched a citizen science project to test the hypothesis that mutations in genes related to neural processes are related to individual variation in loss aversion. Our preliminary experiment showed that DRD2 gene mutations may be related to individual variation in loss aversion. This crowdsourced genetics research is probably the first trial to report the possibilities of individual genetic differences in loss aversion behaviors. We discuss the future paradigms in Ambient Intelligence for health and cognitive enhancement.

One theory of turn-taking in dialogue is that the current speaker controls when the other conversant can speak, which is also the basis of most spoken dialogue systems. A second theory is that the two conversants negotiate who will speak next. In this paper, we testthese theories by examining how well an overhearer can predict this,based only on the current speaker's utterance, which is what the other conversant would have access to. We had overhearers listen to the current speaker and indicate whether they felt the current speaker will continue or not. Our results support the negotiative model.

In this work, we make a contribution to developing turn-taking mechanism in spoken dialogue systems. We focus on modelling the turn-taking behavior in human-human conversations. The proposed models are tested on the Switchboard corpus which contains conversations annotated at the utterance level. Several experiments were performed to analyze the salience of different features that are associated with the preceding utterances for the task of predicting whether there will be a change in speaker. The impact of the n-gram sequential modelling on turn-taking is studied. Machine learning techniques are also employed to perform this prediction task. Results from the experiments suggest that a combination of the preceding dialogue sequence, previous changes in speaker information and duplicating the sequences by replacing speaker IDs plays an important role in modelling turn-taking. Utterance sequences of length 3 in N-grams resulted in higher predictability for this task. Experiments suggest that a machine learning technique with 4-grams of a combination of all these features is effective for predicting speaker changes.

We think that ambient intelligence can expand playing house is one of the most popular play for young children. Therefore, we developed a prototype of cooking application for iPad. And we adopted Tangiblock invented by Bennesse Corporation as natural user interface. For cooking, users tap and rub a screen of the iPad with the ingredient and tool blocks. After that, a dish will be completed and the kind of dish decided automatically by combination of blocks. To make the user imagine eating the dish, we used an appetizing picture to express the dish. And we provided a recipe of the dish too, if they want to make it for real. As a result of observation, the game seemed able to attract children's interest in food, and we have think ambient intelligence has the potential to contribute to food education.

We present a novel probabilistic model and specification language for spatial relations. Qualitative spatial logics such as RCC are used for representation and reasoning about physical entities. Our probabilistic RCC semantics enables a more expressive representation of spatial relations. We observe that reasoning in this new framework can be hard. We address this difficulty by using a factored representation based on Markov Random Fields.

We describe the SRI BioFrustration Corpus, an in-progress corpus of time-aligned audio, video, and autonomic nervous system signals recorded while users interact with a dialog system to make returns of faulty consumer items. The corpus offers two important advantages for the study of turn-taking under emotion. First, it contains state-of-the-art ECG, skin conductance, blood pressure, and respiration signals, along with multiple audio channels and video channels. Second, the collection paradigm is carefully controlled. Though the users believe they are interacting with an empathetic system, in reality the system afflicts each subject with an identical history of "frustration inducers." This approach enables detailed within- and across-speaker comparisons of the effect of physiological state on user behavior. Continuous signal recording enables studying the effect of frustration inducers with respect to speech-based system-directed turns, inter-turn regions, and system text-to-speech responses.

Actions in a multi-agent setting have complex characteristics. They may not only affect the real world, but also affect the knowledge and beliefs of agents in the world. In many cases, the effect on the beliefs or knowledge of an agent is not due to that agent actively doing some actions, but could be simply the result of that agent’s perspective in terms of where it is looking. In dynamic epistemic logic (DEL), such multi-agent actions are expressed as complex constructs or as Kripke model type structures. This paper uses the multi-agent action language mA+ to show how one can take advantage of some of the perspective fluents of the world to model com- plex actions, in the sense of DEL, as simple action sequences. The paper describes several plan modules using such actions. Such plan modules will be helpful in planning for belief and knowledge goals in a multi-agent setting, as planning from scratch would often be prohibitively time consuming.

Formal ontology provides axiomatizations of domain independent principles which, among other applications, can be used to identify modeling errors within a knowledge base. The Ontoclean methodology is probably the best-known illustration of this strategy, but its cost in terms of manual work is often considered dissuasive. This article investigates the applicability of such debugging strategies to Description Logics knowledge bases, showing that even a partial and shallow analysis rapidly performed with a top-level ontology can reveal the presence of violations of common sense, and that the bottleneck, if there is one, may instead reside in the resolution of the resulting inconsistency or incoherence.

In this extended abstract we examine the principles that underlie the construction of what we call Ontologies in Variation — a human-comprehensible knowledge representation scheme for natural kinds, objects, and concepts that captures both prototypical (or canonical) properties of classes of objects as well as those properties that are in variation. A fundamental characteristic of our work is that the variability captured in our representation is derived from data and as such that the provenance of statistical knowledge — the dataset — is directly associated with the ontology. This reliance on empirical data directs us towards a frequentist view of variation as statistical assertions, in contrast to much of the current work that integrates logic and uncertainty. Our formalism's novelty lies in the strategic complementation of axiomatic knowledge by statistical knowledge, and by the desire to preserve human comprehension of the resulting representation. We illustrate this work in the context of an ongoing project to create a representation of human anatomy — a queryable digital anatomy book that fits all of us in some variation.

Psychological evidence supporting the profound effortlessness (and often substantial carelessness) with which human cognition copes with typical daily life situations abounds. In line with this evidence, we propose a formal semantics for computational cognition that places emphasis on the existence of naturalistic and unpretentious algorithms for representing, acquiring, and manipulating knowledge. At the heart of the semantics lies the realization that the partial nature of perception is what ultimately necessitates — and hinders — cognition. Inexorably, this realization leads to the adoption of a unified treatment for all considered cognitive processes, and to the representation of knowledge via prioritized implication rules. Through discussion and the implementation of an early prototype cognitive system, we argue that such fast and loose semantics may offer a good basis for the development of machines with cognitive abilities.