108,869 research outputs found
Towards formal models and languages for verifiable Multi-Robot Systems
Incorrect operations of a Multi-Robot System (MRS) may not only lead to
unsatisfactory results, but can also cause economic losses and threats to
safety. These threats may not always be apparent, since they may arise as
unforeseen consequences of the interactions between elements of the system.
This call for tools and techniques that can help in providing guarantees about
MRSs behaviour. We think that, whenever possible, these guarantees should be
backed up by formal proofs to complement traditional approaches based on
testing and simulation.
We believe that tailored linguistic support to specify MRSs is a major step
towards this goal. In particular, reducing the gap between typical features of
an MRS and the level of abstraction of the linguistic primitives would simplify
both the specification of these systems and the verification of their
properties. In this work, we review different agent-oriented languages and
their features; we then consider a selection of case studies of interest and
implement them useing the surveyed languages. We also evaluate and compare
effectiveness of the proposed solution, considering, in particular, easiness of
expressing non-trivial behaviour.Comment: Changed formattin
Organization of Multi-Agent Systems: An Overview
In complex, open, and heterogeneous environments, agents must be able to
reorganize towards the most appropriate organizations to adapt unpredictable
environment changes within Multi-Agent Systems (MAS). Types of reorganization
can be seen from two different levels. The individual agents level
(micro-level) in which an agent changes its behaviors and interactions with
other agents to adapt its local environment. And the organizational level
(macro-level) in which the whole system changes it structure by adding or
removing agents. This chapter is dedicated to overview different aspects of
what is called MAS Organization including its motivations, paradigms, models,
and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page
Climate Science, Development Practice, and Policy Interactions in Dryland Agroecological Systems
The literature on drought, livelihoods, and poverty suggests that dryland residents are especially vulnerable to climate change. However, assessing this vulnerability and sharing lessons between dryland communities on how to reduce vulnerability has proven difficult because of multiple definitions of vulnerability, complexities in quantification, and the temporal and spatial variability inherent in dryland agroecological systems. In this closing editorial, we review how we have addressed these challenges through a series of structured, multiscale, and interdisciplinary vulnerability assessment case studies from drylands in West Africa, southern Africa, Mediterranean Europe, Asia, and Latin America. These case studies adopt a common vulnerability framework but employ different approaches to measuring and assessing vulnerability. By comparing methods and results across these cases, we draw out the following key lessons: (1) Our studies show the utility of using consistent conceptual frameworks for vulnerability assessments even when quite different methodological approaches are taken; (2) Utilizing narratives and scenarios to capture the dynamics of dryland agroecological systems shows that vulnerability to climate change may depend more on access to financial, political, and institutional assets than to exposure to environmental change; (3) Our analysis shows that although the results of quantitative models seem authoritative, they may be treated too literally as predictions of the future by policy makers looking for evidence to support different strategies. In conclusion, we acknowledge there is a healthy tension between bottom-up/ qualitative/place-based approaches and top-down/quantitative/generalizable approaches, and we encourage researchers from different disciplines with different disciplinary languages, to talk, collaborate, and engage effectively with each other and with stakeholders at all levels
Situating emotional experience
Psychological construction approaches to emotion suggest that emotional experience is situated and dynamic. Fear, for example, is typically studied in a physical danger context (e.g., threatening snake), but in the real world, it often occurs in social contexts, especially those involving social evaluation (e.g., public speaking). Understanding situated emotional experience is critical because adaptive responding is guided by situational context (e.g., inferring the intention of another in a social evaluation situation vs. monitoring the environment in a physical danger situation). In an fMRI study, we assessed situated emotional experience using a newly developed paradigm in which participants vividly imagine different scenarios from a first-person perspective, in this case scenarios involving either social evaluation or physical danger. We hypothesized that distributed neural patterns would underlie immersion in social evaluation and physical danger situations, with shared activity patterns across both situations in multiple sensory modalities and in circuitry involved in integrating salient sensory information, and with unique activity patterns for each situation type in coordinated large-scale networks that reflect situated responding. More specifically, we predicted that networks underlying the social inference and mentalizing involved in responding to a social threat (in regions that make up the “default mode” network) would be reliably more active during social evaluation situations. In contrast, networks underlying the visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations. The results supported these hypotheses. In line with emerging psychological construction approaches, the findings suggest that coordinated brain networks offer a systematic way to interpret the distributed patterns that underlie the diverse situational contexts characterizing emotional life
Exploring the Relationship Between Self-Regulated Learning and Reflection in Teacher Education
Literature on teacher learning has shown links between being a self-regulated learner, reflecting effectively on one’s own practice, and being described as an “adaptive expert”. For instance, the metacognitive skills needed for effective reflection on teaching practice are seen as critically important to developing adaptive expertise in the context of the highly complex classroom environment. Similarly, self-regulated learning is often defined, at least in part, in terms of using metacognitive skill to adapt one’s approach to complex learning situations or problems. Although there is rich literature on reflective practice in teacher education, less is known about measuring teachers’ self-regulated learning or the relationship between self-regulated learning and teacher reflections. This research examines reflective practice and self-regulated learning through pre-service teachers’ written reflections. The study makes a novel adaptation of a rubric designed to evaluate teacher education candidates’ reflections to measure self-regulated learning. Findings suggest that the rubric could also be useful in understanding the self-regulated practices of teacher education candidates
Self-directedness, integration and higher cognition
In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm
The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling
Building machines that learn and think about morality
Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss how work in embodied and situated cognition could provide a valu- able perspective on future research
- …