The Dynamics of Goal Congruency and Cognitive Busyness in Goal Detection

2010 ◽  
Vol 38 (4) ◽  
pp. 517-542 ◽  
Author(s):  
Nicholas A. Palomares
2004 ◽  
Author(s):  
Natalie Hall ◽  
Richard Crisp ◽  
Ifat Rauf ◽  
Terry Eskenazi-Behar ◽  
Russell Hutter ◽  
...  

Robotica ◽  
2018 ◽  
Vol 37 (4) ◽  
pp. 691-707 ◽  
Author(s):  
Mehmet Serdar Güzel ◽  
Vahid Babaei Ajabshir ◽  
Panus Nattharith ◽  
Emir Cem Gezer ◽  
Serhat Can

SummaryThis work addresses a new framework that proposes a decentralized strategy for collective and collaborative behaviours of multi-agent systems. This framework includes a new clustering behaviour that causes agents in the swarm to agree on attending a group and allocating a leader for each group, in a decentralized and local manner. The leader of each group employs a vision-based goal detection algorithm to find and acquire the goal in a cluttered environment. As soon as the leader starts moving, each member is enabled to move in the same direction by staying coordinated with the leader and maintaining the desired formation pattern. In addition, an exploration algorithm is designed and integrated into the framework so as to allow each group to be able to explore goals in a collaborative and efficient manner. A series of comprehensive experiments are conducted in order to verify the overall performance of the proposed framework.


Author(s):  
Eliezer Yudkowsky

By far the greatest danger of Artificial Intelligence (AI) is that people conclude too early that they understand it. Of course, this problem is not limited to the field of AI. Jacques Monod wrote: ‘A curious aspect of the theory of evolution is that everybody thinks he understands it’ (Monod, 1974). The problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard, as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about AI than they actually do. It may be tempting to ignore Artificial Intelligence because, of all the global risks discussed in this book, AI is probably hardest to discuss. We cannot consult actuarial statistics to assign small annual probabilities of catastrophe, as with asteroid strikes. We cannot use calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters. But this makes AI catastrophes more worrisome, not less. The effect of many cognitive biases has been found to increase with time pressure, cognitive busyness, or sparse information. Which is to say that the more difficult the analytic challenge, the more important it is to avoid or reduce bias. Therefore I strongly recommend reading my other chapter (Chapter 5) in this book before continuing with this chapter. When something is universal enough in our everyday lives, we take it for granted to the point of forgetting it exists. Imagine a complex biological adaptation with ten necessary parts. If each of the ten genes is independently at 50% frequency in the gene pool – each gene possessed by only half the organisms in that species – then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation.


2019 ◽  
Vol 36 (11-12) ◽  
pp. 3908-3933 ◽  
Author(s):  
Sylvia L. Mikucki-Enyart

Uncertainty is germane to the in-law experience. Although associations between in-law’s doubts and message production are well documented, less is known about how in-law’s uncertainty corresponds with their message processing and how these evaluations associate with relational outcomes. Weaving theorizing on uncertainty and goal detection, the present study examined associations between children-in-law’s uncertainty, message processing, and marital satisfaction. Self-report, survey data from children-in-law ( n = 199) revealed that their uncertainty corresponds with biased appraisals of their parents-in-law’s communication, specifically perceived topic avoidance and the goals undergirding their avoidant communication (i.e., goal inferences). Additionally, perceived topic avoidance rather than uncertainty shared a stronger association with goal inferences. Finally, goal inferences were directly and indirectly associated with children-in-law’s marital satisfaction. Results are interpreted in light of their theoretical and practical contributions.


1992 ◽  
Vol 4 (3) ◽  
pp. 375-383 ◽  
Author(s):  
Wendy Phillips ◽  
Simon Baron-Cohen ◽  
Michael Rutter

AbstractOne reason for looking at a person's eyes may be to diagnose their goal, because a person's eye direction reliably specifies what they are likely to act upon next. We report an experiment that investigates whether or not young normal infants use eye contact for this function. We placed them in situations in which the adult's action toward them was either ambiguous or unambiguous in its goal. Results showed that the majority of normal infants and young children with mental handicap made instant eye contact immediately following the ambiguous action but rarely after the unambiguous action. Young children with autism, in contrast, made eye contact equally (little) in both conditions. These results are discussed in relation to the function of eye contact, to our understanding of infant cognition, and to the theory of mind hypothesis of autism.


2016 ◽  
Vol 17 (2) ◽  
pp. 155-179
Author(s):  
Nicholas A. Palomares ◽  
Katherine Grasso ◽  
Siyue Li ◽  
Na Li

Abstract An experiment examined goal understanding and how perceivers’ suspiciousness was associated with the accuracy, valence, and certainty of their inferences about a pursuer’s goal. In initial interactions, one dyad member was randomly assigned as the pursuer, and the other was the perceiver. The congruency of the perceiver’s and the pursuer’s conversation goals (i.e., discordant, identical, or concordant) and the perceiver’s cognitive busyness were manipulated. Results confirmed that accuracy decreased as perceivers’ suspiciousness increased only for not-busy perceivers in the goal-discord condition because perceivers’ inferences were negatively valenced. Results also supported the hypotheses that certainty decreased as perceivers’ suspiciousness increased only for not-busy perceivers in the goal-discord condition and that certainty increased as perceivers’ suspiciousness increased both for not-busy perceivers in the identical-goal condition and for busy perceivers in the goal-discord condition.


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