Integrating Twofold Case Retrieval and Complete Decision Replay in CAPlan/CbC

In this thesis, different techniques for performing retrieval, adaptation and learning will be presented and integrated in the case-based planner CAPlan/CbC. The main purpose of this thesis is to improve the performance of the case-based planning process.Integrating Twofold Case Retrieval and Complete Decision Replay in CAPlan/Cb

Assessment of the corneal collagen organization after chemical burn using second harmonic generation microscopy

The organization of the corneal stoma is modified due to different factors, including pathology, surgery or external damage. Here the changes in the organization of the corneal collagen fibers during natural healing after chemical burn are investigated using second harmonic generation (SHG) imaging. Moreover, the structure tensor (ST) was used as an objective tool for morphological analyses at different time points after burn (up to 6 months). Unlike control corneas that showed a regular distribution, the collagen pattern at 1 month of burn presented a non-organized arrangement. SHG signal levels noticeably decreased and individual fibers were hardly visible. Over time, the healing process led to a progressive re-organization of the fibers that could be quantified through the ST. At 6 months, the stroma distribution reached values similar to those of control eyes and a dominant direction of the fibers re-appeared. The present results show that SHG microscopy imaging combined with the ST method is able to objectively monitor the temporal regeneration of the corneal organization after chemical burn. Future implementations of this approach into clinically adapted devices would help to diagnose and quantify corneal changes, not only due to chemical damages, but also as a result of disease or surgical procedures

Applying for National Science Foundation Funding in Cognitive Science: Cognition, Computation, Development, Education, and Neuroscience

Variation in second language acquisition is evident from earliest stages. This study examined effects of learning tasks (retrieval practice, comprehension, verbal repetition) on comprehension of Turkish as a new language. Undergraduates (N = 156) engaged with Turkish spoken dialogues in a computer-assisted language learning session via Zoom, with learning tasks manipulated between-subjects. Participants completed pre/posttests assessing comprehension of Turkish number and case marking, a vocabulary test, and open-response questions gauging explicit awareness. The retrieval-practice group showed highest performance overall, after controlling for significant effects of nonverbal ability and pretest. For comprehension of number/case marking, the comprehension group performed comparably to the retrieval-practice group. For vocabulary comprehension, the verbal-repetition group performed comparably to the retrieval-practice group. Differential performance associated with learning tasks indicates benefits of testing and production and aligns with transfer-appropriate processing. As predicted by the noticing hypothesis, explicit awareness of number and case marking correlated with comprehension accuracy

The Primeval Populations of the Ultra-Faint Dwarf Galaxies

We present new constraints on the star formation histories of the ultra-faint dwarf (UFD) galaxies, using deep photometry obtained with the Hubble Space Telescope (HST). A galaxy class recently discovered in the Sloan Digital Sky Survey, the UFDs appear to be an extension of the classical dwarf spheroidals to low luminosities, offering a new front in efforts to understand the missing satellite problem. They are the least luminous, most dark-matter dominated, and least chemically-evolved galaxies known. Our HST survey of six UFDs seeks to determine if these galaxies are true fossils from the early universe. We present here the preliminary analysis of three UFD galaxies: Hercules, Leo IV, and Ursa Major I. Classical dwarf spheroidals of the Local Group exhibit extended star formation histories, but these three Milky Way satellites are at least as old as the ancient globular cluster M92, with no evidence for intermediate-age populations. Their ages also appear to be synchronized to within ~1 Gyr of each other, as might be expected if their star formation was truncated by a global event, such as reionization.Comment: Accepted for publication in The Astrophysical Journal Letters. Latex, 5 pages, 2 color figures, 1 tabl

SHOP and M-SHOP: Planning with Ordered Task Decomposition

SHOP (Simple Hierarchical Ordered Planner) and M-SHOP (Multi-task-list SHOP) are planning algorithms with the following characteristics. * SHOP and M-SHOP plan for tasks in the same order that they will later be executed. This avoids some task-interaction issues that arise in other HTN planners, making the planning algorithms relatively simple. This also makes it easy to prove soundness and completeness results. * Since SHOP and M-SHOP know the complete world-state at each step of the planning process, they can use highly expressive domain representations. For example, they can do planning problems that require Horn-clause inferencing, complex numeric computations, and calls to external programs. * In our tests, SHOP and M-SHOP were several orders of magnitude faster than Blackbox, IPP, and UMCP, and were several times as fast as TLplan. * The approach is powerful enough to be used in complex real-world planning problems. For example, we are using a Java implementation of SHOP as part of the HICAP plan-authoring system for Noncombatant Evacuation Operations (NEOs). In this paper, we describe SHOP and M-SHOP, present soundness and completeness results for them, and compare them experimentally to Blackbox, IPP, TLplan, and UMCP. The results suggest that planners that generate totally ordered plans starting from the initial state can "scale up" to complex planning problems better than planners that use partially ordered plans

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

IMPACTing SHOP: Foundations for integrating HTN Planning and Multi-Agency

In this paper we describe a formalism for integrating the SHOP HTN planning system with the IMPACT multi-agent environment. Our formalism provides an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT's capabilities for interacting with external agents, performing mixed symbolic/numeric computations, and making queries to distributed, heterogeneous information sources (such as arbitrary legacy and/or specialized data structures or external databases). We show that this agentized version of SHOP will preserve soundness and completeness if certain conditions are met. (This technical report is the updated version of CS-TR-4085) (Also cross-referenced as UMIACS-TR-2000-02

A textual case-based reasoning framework for knowledge management applications

Paper presented at Professionelles Wissenmanagement Erfahrungen und Visionen. Knowledge Management by Case-Based Reasoning: Experience Management as Reuse of Knowledge: pp. 244-253.Knowledge management (KM) systems manipulate organizational knowledge by storing and redistributing corporate memories that are acquired from the organization’s members. In this paper, we introduce a textual casebased reasoning (TCBR) framework for KM systems that manipulates organizational knowledge embedded in artifacts (e.g., best practices, alerts, lessons learned). The TCBR approach acquires knowledge from human users (via knowledge elicitation) and from text documents (via knowledge extraction) using template-based information extraction methods, a subset of natural language, and a domain ontology. Organizational knowledge is stored in a case base and is distributed in the context of targeted processes (i.e., within external distribution systems). The knowledge artifacts in the case base have to be translated into the format of the external distribution systems. A domain ontology supports knowledge elicitation and extraction, storage of knowledge artifacts in a case base, and artifact translation