The past decade has witnessed an increasing interest in the use of virtual coaches in healthcare. This paper describes a virtual coach to provide mindfulness meditation training, and the coaching support necessary to begin a regular practice. The coach is implemented as an embodied conversational character, and provides mindfulness training and coaching support via a web-based application. The coach is represented as a female character, capable of showing a variety of affective and conversational expressions, and interacts with the user via a mixed-initiative, text-based, natural language dialogue. The coach adapts both its facial expressions and the dialogue content to the user’s learning needs and motivational state. Findings from a pilot evaluation study indicate that the coach-based training is more effective in helping users establish a regular practice than self-administered training via written and audio materials. The paper concludes with an analysis of the coach features that contribute to these results, discussion of key challenges in affect-adaptive coaching, and plans for future work.

Our research is directed towards agile supply chains enabling enterprises to quickly respond to individual customer demand. From this perspective, agility encompasses three dimensions of adaptivity: space, time, and economy. Supply chain agility can be achieved by exploiting the most fundamental resource of any enterprise: knowledge. Studying supply chains, we regard all their tiers, participants, and potential relationships, as the search space for fulfilling individual customer demand. We study supply chains from a knowledge-based coordination perspective and regard logistics as the guiding conceptualization. The contribution of this research is a logistics knowledge infrastructure. We report about applying parts of this infrastructure to coordination problems in three selected case studies.

We consider the problem of assessing the sustainability of alternative designs (e.g., for an urban environment) that are assembled from multiple components (e.g., water supply, transportation system, shopping centers, commercial spaces, parks). We model the sustainability of a design in terms of a set of sustainability attributes. Given the (qualitative) preferences and tradeoffs of decision makers over the sustainability attributes, we formulate the problem of identifying sustainable designs as the problem of finding the most preferred designs with respect to those preferences. We show how techniques for representing and reasoning with qualitative preferences can be used to identify the most preferred designs based on the decision maker’s stated preferences and tradeoffs.

In this contribution, it is suggested that argumentation theories may offer the tools to do so. More specifically, the pragmadialectical theory of argumentation (van Eemeren and Grootendorst 1992; 2004) is proposed as a solid instrument for analyzing and evaluating argumentation in consultation, as it not only provides a set of reasonableness criteria for argumentative conduct but also can account for arguers' need to effectively tailor argumentative messages to their recipients. The instrumental value of pragma-dialectics in the field of automated argument selection will be elucidated by means of a case study concerning antibiotics. In doing so, this contribution is closely connected to the paper by Rubinelli, Wierda, Labrie, and O'Keefe (AAAI Spring Symposium 2011) and provides an exploratory investigation of the advantages of a pragma-dialectical approach to the conceptual design of automated health communication systems and autonomous health promotion.

Developments in sophisticated mobile physiological sensors have presented many novel opportunities for monitoring coaching of individuals. In this work, we investigate the ability to utilize physiological data to recognize the state ofa user while exercising. We discuss recognition of user state using data suchas heart rate, respiration rate, and activity level. We also discuss the development of a motivational agent which utilizes the physiological data to help encourage a user during an exercise routine.

Trust is an approach to managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Here we introduce a simple approach to reasoning about trust with logi

It has been argued that causal rules are necessary for representing both implicit side-effects of actions and action qualifications, and there have been a number different approaches for representing causal rules in the area of formal theories of actions. These different approaches in general agree on rules without cycles. However, they differ on causal rules with mutual cyclic dependencies, both in terms of how these rules are supposed to be represented and their semantics. In this paper we show that by adding one more minimization to Lin's circumscriptive causal theory in the situation calculus, we can have a uniform representation of causal rules including those with cyclic dependencies. We also demonstrate that sometimes causal rules can be compiled into logically equivalent (under a proposed semantics) successor state axioms even in the presence of cyclical dependencies between fluents.

As computational agents are increasingly used beyond research labs, their success will depend on their ability to learn new skills and adapt to their dynamic, complex environments. If human users — without programming skills — can transfer their task knowledge to the agents, learning rates can increase dramatically, reducing costly trials. The TAMER framework guides the design of agents whose behavior can be shaped through signals of approval and disapproval, a natural form of human feedback. Whereas early work on TAMER assumed that the agent's only feedback was from the human teacher, this paper considers the scenario of an agent within a Markov decision process (MDP), receiving and simultaneously learning from both MDP reward and human reinforcement signals. Preserving MDP reward as the determinant of optimal behavior, we test two methods of combining human reinforcement and MDP reward and analyze their respective performances. Both methods create a predictive model, H-hat, of human reinforcement and use that model in different ways to augment a reinforcement learning (RL) algorithm. We additionally introduce a technique for appropriately determining the magnitude of the model's influence on the RL algorithm throughout time and the state space.

Temporal encoding schemes using RDF and OWL are often plagued by a massive proliferation of useless "container" objects. Reasoning and querying with such representations is extremely complex, expensive, and error-prone. We present a temporal extension of the Hayes and ter Horst entailment rules for RDFS/OWL. The extension is realized by extending RDF triples with further temporal arguments and requires only some lightweight forms of reasoning. The approach has been implemented in the forward chaining engine HFC.

The paper develops a formal model for interpreting vague languages based on a variant of "supervaluation" semantics. Two modes of semantic variability are modelled, corresponding to different aspects of vagueness: one mode arises where there can be multiple definitions of a term; and the other relates to the threshold of applicability of a vague term with respect to the magnitude of relevant observable values. The truth of a proposition depends on both the possible world and the "precisification" with respect to which it is evaluated. Structures representing possible worlds and precisifications are both specified in terms of primitive functions representing observable measurements, so that the semantics is grounded upon an underlying theory of physical reality. On the basis of this semantics, the acceptability of a proposition to an agent is characterised in terms of a combination of agent's beliefs about the world and their attitude to admissible interpretations of vague predicates.