We present a framework for semantic visual scene interpretation in a system with vision and language. In this framework the system consists of two modules, a language module and a vision module that communicate with each other in a form of a dialogue to actively interpret the scene. The language module is responsible for obtaining domain knowledge from linguistic resources and reasoning on the basis of this knowledge and the visual input. It iteratively creates questions that amount to an attention mechanism for the vision module which in turn shifts its focus to selected parts of the scene and applies selective segmentation and feature extraction. As a formalism for optimizing this dialogue we use information theory. We demonstrate the framework on the problem of recognizing a static scene from its objects and show preliminary results for the problem of human activity recognition from video. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.

Frequently, it is advantageous for an agent to model other agents in order to predict their behavior during an interaction. Modeling others as rational has a long tradition in AI and game theory, but modeling other agents’ departures from rationality is difficult and controversial. This paper proposes that bounded rationality be modeled as errors the agent being modeled is making while deciding on its action. We are motivated by the work on quantal response equilibria in behavioral game theory which uses Nash equilibria as the solution concept. In contrast, we use decision-theoretic maximization of expected utility. Quantal response assumes that a decision maker is rational, i.e., is maximizing his expected utility, but only approximately so, with an error rate characterized by a single error parameter. Another agent’s error rate may be unknown and needs to be estimated during an interaction. We show that the error rate of the quantal response can be estimated using Bayesian update of a suitable conjugate prior, and that it has a finitely dimensional sufficient statistic under strong simplifying assumptions. However, if the simplifying assumptions are relaxed, the quantal response does not admit a finite sufficient statistic and a more complex update is needed. This confirms the difficulty of using simple models of bounded rationality in general settings.

Having discovered a decision principle for a well-known prima facie duty theory in biomedical ethics to resolve particular cases of a common type of ethical dilemma, we developed three applications: a medical ethics advisor system, a medication reminder system and an instantiation of this system in a Nao robot. We are now developing a general, automated method for generating from scratch the ethics needed for a machine to function in a particular domain, without making the assumptions used in our prototype systems.

This paper describes the development of a corpus of blog posts that are annotated for the presence of attempts to persuade and corresponding tactics employed in persuasive messages. We investigate the feasibility of classifying blog posts as persuasive or non-persuasive on the basis of lexical features in the text and the tactics (as provided by human annotators). Annotated tactics provide substantial assistance in classifying persuasion, particularly tactics indicating formal reasoning, deontic obligation, and discussions of possible outcomes, suggesting that learning to identify tactics may be an excellent first step to detecting attempts to persuade.

In this paper, a practical example of implemented high abstraction-level control of mobile robot is presented. A method to represent abstract plans is shown along with a mechanism to schedule the actions within the plans for concurrent execution. Furthermore, a mechanism to consider contingencies and dynamic environment is explained.

The use of automated action planning techniques is essential for efficient mission execution of mobile robots. However, a tremendous effort is needed to represent planning problem domains realistically to meet the real-world constraints. Therefore, there is another source of uncertainty for mobile robot systems due to the impossibility of perfectly representing action representations (e.g., preconditions and effects) in all circumstances. When domain representations are not complete, a planner may not be capable of constructing a valid plan for dynamic events even when it is possible. This research focuses on a generic domain update method to construct alternative plans against real-time execution failures which are detected either during runtime or earlier by a plan simulation process. Based on the updated domain representations, a new executable plan is constructed even when the outcomes of existing operators are not completely known in advance or valid plans are not possible with the existing representation of the domain. A failure resolution scenario is given in the realistic Webots simulator with mobile robots. Since TLPlan is used as the base temporal planner, makespan optimization is achieved with the available knowledge of the robots.

We present an original integration of high level planning and execution with incoming perceptual information from vision, SLAM, topological map segmentation and dialogue. The task of the robot system, implementing the integrated model, is to explore unknown areas and report detected objects to an operator, by speaking loudly. The knowledge base of the planner maintains a graph-based representation of the metric map that is dynamically constructed via an unsupervised topological segmentation method, and augmented with information about the type and position of detected objects, within the map, such as cars or containers. According to this knowledge the cognitive robot can infer strategies in so generating parametric plans that are instantiated from the perceptual processes. Finally, a model-based approach for the execution and control of the robot system is proposed to monitor, concurrently, the low level status of the system and the execution of the activities, in order to achieve the goal, instructed by the operator.

Mainly, we need kinds of virtualization technologies to offer on-demand to identify what parts of the model are really important and computing resources. There is widespread consensus that what other parts are secondary. Then, we would like to study the Future Internet will be heavily based on some kind of the scalability of the current models and identify the current successful Cloud technology. However, to master the deployment limits. Therefore, we propose to consider all existing of Cloud-based infrastructures, some hard scientific CP models in order to answer to these questions.

Despite a dramatic growth of power consumption inhouseholds, less attention has been paid to monitoring,analyzing and predicting energy usage. In this paper,we propose a framework to mine raw energy data bytransforming time series energy data into a symbol se-quence, and then extend a suffix tree data structure asan efficient representation to analyze global structuralpatterns. Then, we use a clustering algorithm to detectenergy pattern outliers which are far from their clustercentroids. To validate our approach, we use real powerdata collected from a smart apartment testbed duringtwo months.

Game theory has become an important tools for making resource allocations decision in security domains, including critical infrastructure protection. Many of these games are formulated as Stackelberg security games. We present new analysis and algorithms for a class of Stackelberg security games with identical, fully protective defender resources. The first algorithm has worst-case complexity linear in the number of possible targets, but works only for a restricted case. The second algorithm can find and optimal resource allocation for the general case in time O(n log(n)).