This paper studies 3-state, 2-direction Mobile Au- tomata. The results of this study show that although it is more difficult to find complexity in Mobile Automata than Cellular Automata, 3-color Mobile Automata can still be divided into four classes of complexity, thus pro- ducing complex behavior. There are 627 number of 3- color Mobile Automata, which were studied and filtered to prove the complexity of Mobile automata. The results of this study infer that it is possible to observe complex- ity in systems that contain only one active cell, if the system has more then two states.

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.

Millions of people globally have been forcibly displaced from their homes due to reasons beyond their control such as conflict, political upheaval, and environmental catastrophes. In many cases, these forced migrants seek temporary refuge in camps managed by nongovernmental organizations (NGOs). Although responsibility for refugees’ well-being within camps belongs mainly to the NGOs and host government, the density of the camp population and lack of resources of service providers leads to a high degree of insecurity. Building off successful models of mHealth, or utilizing mobile technologies to address healthcare needs, this paper explores the possibility of using communication technologies to address personal security issues. Using agent based modeling techniques, this paper examines the ways in which information about incidents of violence are communicated through a closed population. In this way, the authors advocate for the use of mobile phones in an mSecurity context that empowers forced migrants to become active members in reducing incidents of violence within refugee and internally displaced persons camps.

International networks, although highly efficient, may produce surprising threshold effects that shift costs to geographically distant locations. International utility, transportation, and information networks facilitate the efficient flow of information, energy, goods and people. These networks exhibit a scale-free network structure with a few large “hubs”. Yet their efficiency belies their lack of robustness. Because such networks transcend national boundaries, furthermore, disruptions to the network in one geographic region may have profound economic and national security costs for countries in another region. To illustrate how complex networks may transmit costs among countries, this paper builds an agent-based model (ABM) of the international air transportation system. The ABM employs a genetic algorithm to identify “small” disruptions that produce cascading network failures. The study makes two contributions. First, it demonstrates how some complex networks evolve into network structures that trade off robustness for efficiency. Second, it illustrates how researchers can combine agent-based modeling, evolutionary computation, and network analysis to simulate differing failure modes for global networks. This convergence of simulation methodologies characterizes the emerging field of computational social science.

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.

In this paper, we address the problem of communicating, interpreting,and executing complex yet abstract instructions to a robot teammember. This requires specifying the tasks in an unambiguous manner,translating them into operational procedures, and carrying outthose procedures in a persistent yet reactive manner. We reportour response to these issues, after which we demonstrate theircombined use in controlling a mobile robot in a multi-room officesetting on tasks similar to those in search-and-rescue operations.We conclude by discussing related research and suggesting directionsfor future work.

The Betty’s Brain computer-based learning system provides an open-ended and choice-rich environment for science learning. Using the learning-by-teaching paradigm paired with feedback and support provided by two pedagogical agents, the system also promotes the development of self-regulated learning strategies to support preparation for future learning. We apply metacognitive learning theories and experiential analysis to interpret the results from previous classroom studies. We propose an integrated cognitive and metacognitive model for effective, self-regulated student learning in the Betty’s Brain environment, and then apply this model to interpret and analyze common suboptimal learning strategies students apply during their learning. This comparison is used to derive feedback for helping learners overcome these difficulties and adopt more effective strategies for regulating their learning. Preliminary results demonstrate that students who were responsive to the feedback had better learning performance.

This article proposes a suite of design decisions for the overall design of an Artificial Intelligence, i.e., a software system that exhibits intelligence in the spirit of the early days of A.I. research. The key aspects of the proposal are: (1) The identification of the A.I. system as a software individual that has the properties of integrity and persistence; (2) The construction of a software platform that integrates aspects of incremental programming languages and systems as well as of operating systems, with aspects that are intrinsic to knowledge-based artificial intelligence; (3) The use of a representation language that builds on essential aspects of S-expressions, Lisp, logic and extended set theory, but which is used both as a vehicle for software and as a publication language e.g. in lecture notes; (4) The identification of actions and aggregates of actions as first-class citizens in the representation language and as an important type of data object in the software system. The article also describes the Leonardo software platform, its representation language, its educational resources and its knowledgebase library which is one implementation of these proposed design decisions. Finally it makes a proposal concerning the research paradigm for this research area.

This paper discusses the challenge of designing instructable agents that can learn through interaction with a human expert. Learning through instruction is a powerful paradigm for acquiring knowledge because it limits the complexity of the learning task in a variety of ways. To support learning through instruction, the agent must be able to effectively communicate its lack of knowledge to the human, comprehend instructions, and apply them to the ongoing task. Weidentify some problems of concern when designing instructable agents. We propose an agent design that addresses some of these problems. We instantiate this design in the Soar cognitive architecture and analyze its capabilities on a learning task.

In this paper we discuss an elaboration account of insight that provides answers to two of the main questions regarding insight problem solving: why insight problems are so difficult for humans and why insight is so rapid in nature. We claim that the difficulty in insight problems is due to misguided heuristic search and that this difficulty is overcome using a reformulation mechanism. Furthermore, we claim that search is carried out quickly when the heuristics are good--explaining the rapid nature of insight. We clarify our account by providing examples and initial empirical results. In conclusion, we review related work and discuss possible future work.