For intelligent agents modeled to emulate people, reference resolution is memory management: when processing an object or event – whether it appears in language or in the simulated physical or cognitive experience of the agent – the agent must determine how that object or event correlates with known objects and events, and must store the new memory with semantically explicit links to related prior knowledge. This paper discusses eventualities for memory-based reference resolution and the modeling strategies used in the OntoAgent environment to permit agents to fully and automatically make reference decisions.

Broadly speaking, information has something to do with order or organization within a system of elements. The thermodynamic concept of entropy is also associated with such systems, although in an inverse relationship. When we attempt to put these two apparently coordinated schemas of order and disorder together, all kinds of difficulties arise. I will briefly examine contemporary efforts to unify these two ways of conceiving order and show that they are substantially incompatible. In this process I will draw some distinctions that will lead to a broader reconciliation of the concepts of order and information. I will then attempt to re-evaluate the fundamental models behind these dissonant traditions for formulating order in an attempt to reframe a synthesis of conceptual structures that are mutually reconcilable. I will try to show that such a synthesis can finally make sense of the stubborn inconsistencies that persist in the ways Newtonian dynamics, thermodynamics and biology utilize the implicitly conflicting arrows of time.

We present an evaluation methodology and a system for ranking questions within the context of a multimodal tutorial dialogue. Such a framework has applications for automatic question selection and generation in intelligent tutoring systems. To create this ranking system we manually author candidate questions for specific points in a dialogue and have raters assign scores to these questions. To explore the role of question type in scoring, we annotate dialogue turns with labels from the DISCUSS dialogue move taxonomy. Questions are ranked using a SVM-regression model trained with features extracted from the dialogue context, the candidate question, and the human ratings. Evaluation shows that our system’s rankings correlate with human judgments in question ranking.

Information in bicycle pelotons consists of two main types: displayed information that is perceptible to others; and hidden information available to individual riders about their own physical state. Flow (or transfer) of information in pelotons occurs in two basic ways: 1) between cyclists within a peloton, which riders exploit to adjust tactical objectives (“intra-peloton”); 2) from sources outside a peloton as it is fed to riders via radio communication, or from third parties (“extra-peloton”). A conceptual framework is established for information transfer intra-peloton and extra-peloton. Both kinds of information transfer affect peloton complex dynamics. Pelotons exhibit mixed self-organized and top-down dynamics. These can be isolated and examined independently: self-organized dynamics emerge through local physical rules of interaction, and are distinguishable from the top-down dynamics of human competition, decision-making and information transfer. Both intra and extra-peloton information flow affect individual rider positions and the timing of their positional changes, but neither types of peloton information flow fundamentally alter self-organized structures. In addition to two previously identified peloton resources for which riders compete - energy saved by drafting, and near-front positions - information flow is identified as a third peloton resource. Also, building upon previous work on peloton phase-transitions and self-organized group-sorting, identified here is a transition between a team cluster state in which team-mates ride near each other, and a self-organized “fitness” cluster state in which riders of near equal fitness levels gravitate toward each other.

In this paper, we propose a simulation platform for the performance testing of robots and human operations. Robots have been used in disaster scenarios, where the environment is unstable. Human operators may have no prior experience in dealing with such dynamically changing environments, which may also be unstable for robotic tasks. To develop rescue robots, disaster situation emulation and human-in-loop test platform are required in addition to robot simulators. The proposed platform is used to design, develop robots and to conduct drills for robot operations, and to carry out experiments. And the results of experiments are presented.

We present a bi-threshold model of complex contagion in networks. In this model a node in a network can be in one of two states at any time step, and changes state if enough of its neighbors are in the opposite state, as determined by “up-threshold” and “down-threshold” parameters. This dynamical process models several types of social contagion processes, such as public health concerns and the spread of games on online networks. Motivated by recent literature calling for the investigation of peer pressure to reduce obesity, which can be viewed as a control problem of population dynamics, we focus on the computational complexity of finding critical sets of nodes, which are nodes that we choose to freeze in state 0 (a desirable state) in order to inhibit the spread of an undesirable state 1 in the network. We define a minimum-cost critical set problem and show that it is NP-complete for bi-threshold systems. We show that several versions of the problem can be approximated to within a factor of O(log n), where n is the number of nodes in the network. Using the ideas behind these approximations, we devise a heuristic, called the Maximum Contributor Heuristic (MCH), which can be used even when the diffusion model is probabilistic. We perform simulations with well-known networks from the literature and show that MCH outperforms the High Degree Heuristic by several orders of magnitude.

The new scenarios of contemporary adaptive robotics seem to suggest a transformation of the traditional methods. In the search for new approaches to the control of adaptive autonomous systems, the mind becomes a fundamental source of inspiration. In this paper we anticipate, through the use of simulation, the cognitive and behavioral properties that emerge from a recent prototype robotic platform, EcoBot, a family of bio-mechatronic symbionts provided with an `artificial metabolism', that has been under physical development during recent years. Its energy reliance on a biological component and the consequent limitation of its supplied energy determine a special kind of dynamic coupling between the robot and its environment. Rather than just an obstacle, energetic constraints become the opportunity for the development of a rich set of behavioral and cognitive properties.

The concept of common ground — the mutual understanding of context and conventions — is central to philosophical accounts of mendacity; its use is to determine the meaning of linguistic expressions and the significance of physical acts, and to distinguish certain statements as conveying a conventional promise, warranty, or expectation of sincerity. Lying necessarily involves an abuse of common ground, namely the willful violation of conventions regulating sincerity. The ‘lying machine’ is an AI system that purposely abuses common ground as an effective means for practicing mendacity and lesser deceptions. The machine's method is to conceive and articulate sophisms — perversions of normative reason and communication — crafted to subvert its audience's beliefs. Elements of this paper (i) explain the described use of common ground in philosophical accounts of mendacity, (ii) motivate arguments and illusions as stratagem for deception, (iii) encapsulate the lying machine's design and operation, and (iv) summarize human-subject experiments that confirm the lying machine's arguments are, in fact, deceptive.

This paper discusses the implementation and evaluation of automatically generated cloze questions in the style of the definitions found in Collins COBUILD English language learner’s dictionary. The definitions and the cloze questions are used in an automated reading tutor to help second and third grade students learn new vocabulary. A parser provides syntactic phrase structure trees for the definitions. With these parse trees as input, a pattern matching program uses a set of syntactic patterns to extract the phrases that make up the cloze question answers and distracters.

We discuss several improvements to the Question Generation Shared Task Evaluation Challenge (QGSTEC) system developed at the University of Pennsylvania in 2010. In addition to enhancing the question generation rules, we have implemented two new components to improve the ranking process. We use topic scoring, a technique developed for summarization, to identify important information for questioning, and language model probabilities to measure grammaticality. Preliminary experiments show that our approach is feasible.