This paper begins with a discussion of two features of the everyday task environment. First, the everyday task environment is designed, and an important part of the design is the provision of explicit information to guide the individual in the adaptation of his activity. Second, some task environments are semi‐permanent. These two features of the task environment reveal some important characteristics in the psychology of the individual. When novelty occurs, expansion in the range of behavior of the individual is guided (...) by a process of internalization of information provided in the task environment by another. Because of the semi‐permanence of home task environments, there is pay‐off in organizing behavior in terms of the particulars of those environments. The body of this paper examines these ideas from the perspective of FLOABN. FLOABN is a computational cognitive model of an individual acquiring skill at using household and office devices. (shrink)

The Covid-19 pandemic has been analysed and discussed from many disciplinary perspectives. An aspect that still needs critical exploration is the role—that is, the modes and forms—of regulatory interventions during the pan- demic. It is interesting to note in this regard that, in many studies, regulatory measures are labelled “non-pharma- ceutical interventions”, as if they do not have any specificity on their own and only represent a theoretically residual category. The main aim of this article is instead to focus on (...) the distinctive features of normative measures as such. As regards the article’s focus, it centres on the normative interventions in the first period of the pandemic—that is, 2020—with particular reference to Italy. We have chosen to focus on this period because the most extensive and severe restrictions introduced to combat Covid-19 were established at that time. And we have chosen to con- sider Italy because it was the first country, after China, to be hard hit by the virus and to react in a draconian manner. As regards the methodology, the article is based, in general and primarily, on an extensive interdisciplinary literature review. With reference to the Italian case, the study is additionally based on: first-hand data collection and analysis (especially concerning Italian normative measures mainly issued at the national level: their type, frequency, target, hierarchical relationships); second-hand data and analysis (for instance as regards the effectiveness of Italy’s and other countries’ regulatory measures). In discussing the Italian response to the Covid-19 pandemic, certain weaknesses have been identified; and possible lessons have been highlighted, in terms of both “planning” (i.e. the necessity to better pre-define concrete and circumscribed sets of actions) and of “meta-planning” (i.e. the necessity of better planning the way in which public authorities could and should plan and act). Although this article is mainly based on the Italian situation, what we can learn from this case is largely generalizable. (shrink)

This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real-world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem-solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which (...) provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning. (shrink)

Taking a detour to reach a goal is intelligent behavior based on making inferences. The main purpose of the present research is to show how such apparently complex behavior can emerge from basic mechanisms such as contextual categorisation and goal attribution when perceiving people. We presentacacia (Action by Contextually Automated Categorising Interactive Agents), a computer model implemented using StarLogo software, grounded in the principles of Artificial Life (Al), capable of simulating the behavior of a group of agents with a goal (...) (for instance, to find a treasure in a treasure hunt ) in an environment where obstacles mask the goal site. The results of the simulations show that agents reach the goal the fastest when they follow each other and take detours. We argue that these results indicate that intelligent adaptive behavior is based on the contextual categorisation of environmental constrainst (that is, obstacles and other agents). (shrink)

Adaptive Planning is an approach to planning in the commonsense domain. An adaptive planner takes advantage of the habitual nature of many of the planning situations for which it plans by bosing its activities on a memory of pre‐stored plans. A critical issue, and the subject of this paper, is the question of flexibility: How does an adaptive planner refit an old plan in order to meet the demands of some new planning situation? An adaptive planner refits prestored plans by (...) constructing an interpretation of the situation in which it is engaged. Key elements in the theory of adaptive planning are its treatment of specific plans and a notion of situation matching. The theory of adaptive planning has been modeled by a computer program called PLEXUS. (shrink)