In recent work, Langley et al. (2014) introduced UMBRA, a systemfor plan and dialogue understanding. The program applies a form of abductive inference to generate explanations incrementally from relational descriptions of observed behavior and knowledge inthe form of rules. Although UMBRA's creators described the systemarchitecture, knowledge, and inferences, along with experimental studies of its operation, they did not provide a formalization of its structures or processes. In this paper, we analyze both aspects  of the architecture in terms of the Situation Calculus — a classicallogic for reasoning about dynamical systems — and give a specification of the inference task the system performs. After this, we state some properties of this formalization thatare desirable for the task of incremental dialogue understanding. We conclude by discussing related work and describing our plans for additional research.

This paper presents an agent-based model of the emergence and transmission of a language system for the expression of logical combinations of propositions. The model assumes the agents have some cognitive capacities for invention, adoption, repair, induction and adaptation, a common vocabulary for basic categories, and the ability to construct complex concepts using recursive combinations of basic categories with logical categories. It also supposes the agents initially do not have a vocabulary for logical categories (i.e. logical connectives), nor grammatical constructions for expressing logical combinations of basic categories through language. The results of the experiments we have performed show that a language system for the expression of logical combinations emerges as a result of a process of self-organisation of the agents' linguistic interactions. Such a language system is concise, because it only uses words and grammatical constructions for three logical categories (i.e. and, or, not). It is also expressive, since it allows the communication of logical combinations of categories of the same complexity as propositional logic formulas, using linguistic devices such as syntactic categories, word order and auxiliary words. Furthermore, it is easy to learn and reliably transmitted across generations, according to the results of our experiments.

Solution concepts from cooperative game theory, such as the Shapley value or the Banzhaf index, have recently been advocated as interesting extensions of standard measures of node centrality in networks. While this direction of research is promising, the computation of game-theoretic centrality can be challenging. In an attempt to address the computational issues of game-theoretic network centrality, we present a generic framework for constructing game-theoretic network centralities. We prove that all extensions that can be expressed in this framework are computable in polynomial time. Using our framework, we present the first game-theoretic extensions of weighted and normalized degree centralities, impact factor centrality,distance-scaled and normalized betweenness centrality,and closeness and normalized closeness centralities.

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

Saving energy is a major concern. Hence, it is fundamental to design and construct buildings that are energy-efficient. It is known that the early stage of architectural design has a significant impact on this matter. However, it is complex to create designs that are optimally energy efficient, and at the same time balance other essential criterias such as economics, space, and safety. One state-of-the art approach is to create parametric designs, and use a genetic algorithm to optimize across different objectives. We further improve this method, by aggregating the solutions of multiple agents. We evaluate diverse teams, composed by different agents; and uniform teams, composed by multiple copies of a single agent. We test our approach across three design cases of increasing complexity, and show that the diverse team provides a significantly larger percentage of optimal solutions than single agents.

Vehicle-sharing (ex: bike sharing, car sharing) is widelyadopted in many cities of the world due to concernsassociated with extensive private vehicle usage, whichhas led to increased carbon emissions, traffic conges-tion and usage of non-renewable resources. In vehicle-sharing systems, base stations are strategically placedthroughout a city and each of the base stations containa pre-determined number of vehicles at the beginningof each day. Due to the stochastic and individualisticmovement of customers, typically, there is either con-gestion (more than required) or starvation (fewer thanrequired) of vehicles at certain base stations. As demon-strated in our experimental results, this happens oftenand can cause a significant loss in demand. We proposeto dynamically redeploy idle vehicles using carriers soas to minimize lost demand or alternatively maximizerevenue of the vehicle sharing company. To that end,we contribute an optimization formulation to jointly ad-dress the redeployment (of vehicles) and routing (of car-riers) problems and provide two approaches that rely ondecomposability and abstraction of problem domains toreduce the computation time significantly. Finally, wedemonstrate the utility of our approaches on two realworld data sets of bike-sharing companies.

We built a conversational agent performing social promotion (CASP) to assist in automation of interacting with Facebook friends. CASP relies on a domain-independent natural language relevance technique which filters web mining results to support a conversation with friends and other network members. In this study we focus on recognizing friends’ intents to better support automated conversation with them. We learn the plausible sequences of communicative actions and mental states as they are expressed in text to support plausible dialogue. We evaluate the relevance of the constructed conversations with respect to suitability of topicality and communicative actions, measuring how human users loose trust in the system. It is confirmed that maintaining a plausible sequences of communicative actions in automated postings is important for retaining trust of human peers and efficient social promotion by means of CASP.

This paper outlines a new approach to creating value from the Smart Grid by incorporating individual households into the response system that must be deployed to accommodate increasingly large sources of intermittent renewable power. We propose a framework that couples agent-based AI techniques with envelope methods. Envelope methods provide a unified mathematical framework to model intermittent renewable resources, conventional dispatchable resources, demand side response, and storage. The overall goal of our system is to develop a distributed autonomous agent architecture that is able to facilitate market transactions among load serving entities, residential consumers, conventional merchant power producers, and intermittent power producers.

Ensuring that autonomous systems work ethically is both complex and difficult. However, the idea of having an additional ‘governor’ that assesses options the system has, and prunes them to select the most ethical choices is well understood. Recent work has produced such a governor consisting of a ‘consequence engine’ that assesses the likely future outcomes of actions then applies a Safety/Ethical logic to select actions. Although this is appealing, it is impossible to be certain that the most ethical options are actually taken. In this paper we extend and apply a well-known agent verification approach to our consequence engine, allowing us to verify the correctness of its ethical decision-making.

We analyze the results of assistance to individuals with autism in reasoning about mental world. This assistance is provided by a natural language multiagent simulator of mental states, NL_MAMS (Galitsky 2013b). It assists in the tasks which are the hardest for autistic reasoning: operating with mental states and actions. Autistic patients are trained to perform a number of reasoning exercises. We conduct both short term and long term evaluations including the behavior in real world and confirm that the system has a positive effect on their rehabilitation.