Human wayfinding operates very very differently from traditional deterministic algorithms owing to a) restrictions in working memory resulting in subjective regionalized maps, and b)flexible adoption of different navigation strategies. While a number of cognitive strategies have been proposed for human wayfinding, these have been hard to evaluate thoroughly owing to a lack of computational simulation. In this work, we propose a stochastic approach for capturing these aspects, and argue for a memoryless, stationary implementation. In two longitudinal experiments on the same group of subjects, we first estimate the subjective regionalized maps for each subject on the same familiar spatial domain. Later, based on their wayfinding responses, we can estimate the stationary probabilities for different strategies. We apply this algorithm to evaluate three wayfinding strategies proposed in the literature, and repudiate the previously held suggestion that they are followed equiprobably.

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.

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.

This research aims to define a novel framework to employ engineering and mathematical models to study adaptive dynamics in heterarchial systems. This multi-profile descriptive platform and modeling approach is developed as a composite of conceptual behaviors and structural entity aspects of engineered complex adaptive systems (ECAS). While the US electric power system will be utilized for demonstration and validation, the framework has applicability to the general class of ECASs that are artificially created but highly interactive with natural and behavioral sciences. Conditioned on parameterization of the framework, a theorem will be presented to calibrate current structure and predict future dynamic behaviors of an ECAS. We analyze decentralized heterarchial ECASs to infer emergent behavior of the components, and evolution processes and adaptations of the whole system.

Learning to use an object as a tool requires understanding what goals it helps to achieve, the properties of the tool that make it useful and how the tool must be manipulated to achieve the goal. We present a method that allows a robot to learn about objects in this way and thereby employ them as tools. An initial hypothesis for an action model of tool use is created by observing another agent accomplishing a task using a tool. The robot then refines its hypothesis by active learning, generating new experiments and observing the outcomes. Hypotheses are updated using Inductive Logic Programming. One of the novel aspects of this work is the method used to select experiments so that the search through the hypothesis space is minimised.

In this paper we describe a complex adaptive systems model of interactions between coupled human and natural system. We use learning classifier systems to create adaptive agents in a simulation of the Maine lobster fishery to explore the relationships among ecological, economic, and social characteristics. Our hypothesis is that the cost of information and learning drives agents' decisions to compete or co-operate and, consequently, the emergence of long-term relationships. Initial results provide tentative support for the hypothesis and the ability of this model to provide insight into the dynamics of individual interactions and the social relationships that emerge from those interactions.

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.

The literature on conceptual blending and metaphor-making has illustrations galore of how these mechanisms may support the creation and grounding of new concepts (or whole domains) in terms of a complex, integrated network of older ones. In spite of this, as of yet there is no general computational account of blending and metaphor-making that has proven powerful enough as to cover all the examples from the literature. This paper proposes a logic-based framework for blending and metaphor making and explores its applicability in settings as diverse as mathematical domain formation, classical rationality puzzles, and noun-noun combinations.

Question ambiguity is one major factor that affects question quality. Less ambiguous questions can be produced by using more specific question words. We attack the problem of how to ask more specific questions by supplementing question words with the hypernyms for answer phrases. This dramatically increases the coverage of generated "which" questions. Evaluation results show improved question quality when the question words are disambiguated correctly given the context.

We present a graph-theoretic model of discourse based on the Questions Under Discussion (QUD) framework. Questions and assertions are treated as edges connecting discourse states in a rooted graph, modeling the introduction and resolution of various QUDs as paths through this graph. The amount of common ground presupposed by interlocutors at any given point in a discourse corresponds to graphical depth. We introduce a new task-oriented dialogue corpus and show that experts, presuming a richer common ground, initiate discourse at a deeper level than novices. The QUD-graph model thus enables us to quantify the experthood of a speaker relative to a fixed domain and to characterize the ways in which rich common ground facilitates more efficient communication.