Collected notes from the Benchmarks and Metrics Workshop

In recent years there has been a proliferation of proposals in the artificial intelligence (AI) literature for integrated agent architectures. Each architecture offers an approach to the general problem of constructing an integrated agent. Unfortunately, the ways in which one architecture might be considered better than another are not always clear. There has been a growing realization that many of the positive and negative aspects of an architecture become apparent only when experimental evaluation is performed and that to progress as a discipline, we must develop rigorous experimental methods. In addition to the intrinsic intellectual interest of experimentation, rigorous performance evaluation of systems is also a crucial practical concern to our research sponsors. DARPA, NASA, and AFOSR (among others) are actively searching for better ways of experimentally evaluating alternative approaches to building intelligent agents. One tool for experimental evaluation involves testing systems on benchmark tasks in order to assess their relative performance. As part of a joint DARPA and NASA funded project, NASA-Ames and Teleos Research are carrying out a research effort to establish a set of benchmark tasks and evaluation metrics by which the performance of agent architectures may be determined. As part of this project, we held a workshop on Benchmarks and Metrics at the NASA Ames Research Center on June 25, 1990. The objective of the workshop was to foster early discussion on this important topic. We did not achieve a consensus, nor did we expect to. Collected here is some of the information that was exchanged at the workshop. Given here is an outline of the workshop, a list of the participants, notes taken on the white-board during open discussions, position papers/notes from some participants, and copies of slides used in the presentations

BEHAVIORAL INTERDEPENDENCE IN PROJECT TEAM COLLABORATION: STUDY OF ENGINEERING STUDENTS’ COLLABORATIVE BEHAVIORS IN HIGH LEVELS OF INTERDEPENDENT TASK SETTINGS

In teamwork learning settings, tasks are often designed at varying levels of interdependence that requires students to complete the tasks by relying only on their team members sharing resources, knowledge, and skills. However, well-structured tasks do not always guarantee task-related collaborative behaviors will occur and are simply not adequate for us to understand the collaboration process and participants’ actual collaborative behaviors. To deepen our understanding of collaboration and explore how increased collaboration may be promoted in high-level interdependent task settings, this study uses behavioral interdependence as an analytical concept to describe and examine individual students’ actual behaviors as they worked collaboratively on an interdependently-structured engineering design project. Behavioral interdependence is “the amount of task-related interaction actually engaged in by group members in completing their work” (Wageman, 2001, p. 207). The concept of behavioral interdependence helps us to understand students’ task-related collaborative behaviors. However, this concept has received scarce attention in collaboration literature. This study was set in a context of college engineering students collaborating on an authentic design project. A descriptive, instrumental two-case study methodology was employed to respond to two main research questions: (1) what individual behaviors are observed in project teams when students were working under the high task interdependence condition and (2) what patterns of team behaviors are observed in such a condition. After examining and comparing two newly-formed college student project teams’ collaborative behaviors in solving an interdependently-structured engineering design project, answers to the research questions help explore how team behavioral patterns formed out of, or were affected by, students’ individual behaviors and how behaviors affected team collaboration and performance. This study resulted in rich descriptions of individual student behaviors and behavior changes, team behaviors and behavior changes, and how individual behaviors were related to team behaviors and overall team collaboration and performance. Results suggested that (1) individual behaviors were closely associated with team behaviors, collaboration, and performance, (2) students’ early behavioral patterns largely predicted their continuous behaviors, (3) urgent deadlines were likely to change behaviors of students who had poor performance in task management and temporal planning, (4) individuals performing better in disciplinary, technical areas tended to have more contribution to and better participation in teamwork, and (5) teams with high levels of behavioral interdependence tended to have better performance in teamwork. Several recommendations are provided for designing instruction in high interdependent task settings such as careful estimation of task completion time considering students’ varying collaboration skills and time management ability levels (task / activity design recommendation), providing suitable scaffolding strategies to support students who are not adequate in technical fields or in skills in areas of self-management, effective communication, and temporal planning (activity preparation recommendation), and paying attention to students’ behaviors at the early stage of their collaboration and providing timely corrective feedback (formative evaluation recommendations)

Mapping AI Arguments in Journalism Studies

This study investigates and suggests typologies for examining Artificial Intelligence (AI) within the domains of journalism and mass communication research. We aim to elucidate the seven distinct subfields of AI, which encompass machine learning, natural language processing (NLP), speech recognition, expert systems, planning, scheduling, optimization, robotics, and computer vision, through the provision of concrete examples and practical applications. The primary objective is to devise a structured framework that can help AI researchers in the field of journalism. By comprehending the operational principles of each subfield, scholars can enhance their ability to focus on a specific facet when analyzing a particular research topic

Essential Learning Objectives For Graphic Designers, Post Secondary

Graphic designers today need to possess a wide variety of skill sets in order to adequately perform their duties. The skill sets include areas of study in Art, Design, Computer Technology, and Print Technology. My goal is to find what learning objectives in these areas and others are essential for a person to become a graphic designe

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

Commentary on Producing Environmental Information from Stakeholder Engagement

This article provides practice-informed advice, guided by and responsive to theory, for policymakers who seek to improve their environmental policies by generating their own informational value from their interactions with stakeholders. First, the article explains a self-reinforcing opaqueness of conventional environmental policymaking and how this opaqueness disproportionately and cumulatively impacts underrepresented communities. Drawing from the literature of social ecology, political economy, and political methodology, the article adumbrates opaqueness’ contributions to environmental injustice and identifies potential benefits of a more informative approach to stakeholder engagement. Next, the article explains specific methods that policymakers can use to convert stakeholder input into greater awareness of the costs, benefits, risks, and tradeoffs presented by a given environmental policymaking. By conducting stakeholder engagement as an interactive and investigative pursuit, oriented toward underrepresented constituencies as well as policymakers’ own problem statement, policymakers can better inform their policies while also improving relationships with the parties those policies affect

Planning and Team Shared Mental Models as Predictors of Team Collaborative Processes

This study evaluates the role of team planning and the similarity of team shared mental models (TSMMs) as predictors of two types of collaborative behaviors that are known to contribute to team performance. A computer-based Networked Fire Chief (NFC) simulation task was used as a testing environment for emergent and dynamic situations. The relationships among team planning, similarity of task-focused team shared mental models (TASKTSMMs), similarity of team-focused team shared mental models (TEAMTSMMs), team backup behaviors, and implicit coordination were tested. This study provides evidence for the mediation effect of similarity of TASKTSMMs between team planning and team backup behaviors, and the mediation effect of team backup behaviors between similarity of TASKTSMMs and team performance. The results suggest that better team planning is more likely to encourage more backup behaviors and improved performance through teams having more similar task-focused mental models. Both the theoretical and practical implications were discussed and the limitations and future research were also addressed in the study

Searching for a suitable patent system for Finnish SMEs

Suomalaiset pienet ja keskisuuret yritykset (pk-yritykset) haluavat olla mukana maailmanlaajuisessa teknologisessa kehityksessä ja kasvattaa liiketoimintaansa erilaisten innovaatioiden avulla. Näitä innovaatioita pitää suojella kansallisesti ja kansainvälisesti. Lainsäädäntö on kehittänyt tätä varten immateriaalioikeudet, joista tässä työssä käsitellään patenttia. Patentti antaa yksinoikeuden keksintöön sekä suojaa teknisiä ja teollisia innovaatioita väärinkäytöltä. Samalla se voi rajata kilpailua. Monet suomalaiset pk-yritykset ovat kasvavassa määrin hyödyntämässä patenttisuojaa omiin innovaatioihinsa, mutta koko ajan lisääntyvä patenttidatan ja -työkalujen määrä sekä hajanaisuus aiheuttavat haasteita oikean patenttijärjestelmän löytämiselle. Tässä työssä käsitellään neljää patenttijärjestelmää, kansainvälinen, eurooppa-, yhtenäis- ja kansallinen patenttijärjestelmä, koska ne antavat laajan vertailupohjan sekä ovat suomalaisten pk-yritysten innovaatioiden suojaamiseen mahdollisia järjestelmiä. Patenttia suunnittelevien suomalaisten pk-yritysten olisi hyvä ymmärtää patenttilainsäädäntö sekä patenttijärjestelmät. Lisäksi niiden tulisi ottaa huomioon erilaiset ulkoiset tekijät, kuten ympäristönsuojelu ja muut lait. Yrityksellä pitäisi olla myös valmiina riittävän selkeä liiketoiminta- sekä taloudellinen suunnitelma. Tämä tutkielma pyrkii vastaamaan näihin haasteisiin tarjoamalla suomalaisille pk-yritykselle sopivat patenttityökalut oikean patenttijärjestelmän löytämiselle. Pk-yritysten liiketoimintaympäristön, koon ja innovaatiobudjetin vaihtelun vuoksi työ jättää yksittäiselle yritykselle mahdollisuuden itse valita tietty patenttijärjestelmä omien rajojensa puitteissa. Tutkielma on toteutettu oikeusdogmaattisena tutkimuksena laajan oikeudellisen aineiston, kuten lakien ja oikeuskirjallisuuden, avulla. Lisäksi siinä hyödynnetään monia kaupallistaloudellisia lähteitä, joita ovat esimerkiksi tieteelliset artikkelit, tutkimukset sekä tilastot. Patenttityökaluja on monia, ja ne voidaan jakaa patenttijohtamisen, -strategian, taloudellisten laskelmien sekä patenttidataan pohjautuviin (prior art) työkaluihin. Näiden työkalujen avulla pk-yritykset saavat kokonaiskuvan eri patenttinäkökulmista ja pystyvät valitsemaan itselleen sopivan patenttijärjestelmän laajasta patenttidatasta. Kaikki työkalut ovat tärkeitä, mutta niiden hyödyntämisen laajuutta tulee harkita jokaisessa yrityksessä erikseen, jotta yritysten omat tavoitteet voidaan saavuttaa. Tutkimustulokset tarjoavat paljon tärkeää tietoa patenttihakemuksesta. Taustatyö täytyy kuitenkin toteuttaa ensin huolellisesti, mikä tarkoittaa taloudellisen ja liiketoiminnallisen suunnittelun tärkeyttä ennen laajempien innovaatiosuunnitelmien täytäntöönpanoa. Toisaalta pk-yritysten tulee ottaa huomioon ulkoiset teknologiset, lainsäädännölliset, taloudelliset ja ympäristölliset tekijät. Patenttijärjestelmien näkökulmasta kustannukset, hakemusajat, oikeudenkäynnit, luottamus järjestelmään, maantieteellinen laajuus, saatavuus ja patenttihakemuksen vaatimukset vaikuttavat tutkimuksen lopputulokseen