Towards computational models of intention detection and intention prediction

Bonchek-Dokow, Elisheva; Kaminka, Gal A.

doi:10.1016/j.cogsys.2013.07.004

Cited by 18 publications

(19 citation statements)

References 52 publications

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“…The PowerTech contest requires process improvement including finding new information or ideas to solve problems and verifying the new information to enhance the functional quality of the miniature for competition (Authors, 2013). By predicting the intention, Bonchek-Dokow and Kaminka (2014) argued that the objective of winning a competition can extend participants' actions to continue engagement. Supporting this argument, the result of this study showed that a high level of the two types of epistemic curiosity led to a high level of continuance intention to engage in the PowerTech contest.…”

Section: Discussionmentioning

confidence: 99%

Positive Affect Relevant to Epistemic Curiosity to Reflect Continuance Intention to Join a Hands-On Making Contest

Hong¹,

Hwang

Szeto

et al. 2016

EURASIA J MATH SCI T

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Hands-on making (e.g., "Maker") has become prevalent in current educational settings. To understand the role that students' epistemic curiosity plays in hands-on making contests, this study explored its correlation to students' positive affect and continuance intention to participate in a hands-on making contest called "PowerTech". PowerTech requires students to produce miniatures in the morning and use the miniatures to take part in relay racing and tug-of-war competitions in the afternoon. Data from 514 individuals were collected and subjected to confirmatory factor analysis and structural equation modeling by AMOS 20. The results revealed the two types of epistemic curiosity (EC), interest (I-type) and informational deprivation type (D-type), played a mediating role in bonding participants' positive affect and their continuance intention to join a hands-on making contest. The implication of this study suggests students' epistemic curiosity can be aroused in the process of participating in competitive handson making contests such as PowerTech.

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Section: Discussionmentioning

confidence: 99%

Positive Affect Relevant to Epistemic Curiosity to Reflect Continuance Intention to Join a Hands-On Making Contest

Hong¹,

Hwang

Szeto

et al. 2016

EURASIA J MATH SCI T

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“…They characterise goal recognition as "goal mirroring" (that is, the empathetic human response to observations, whereby we imagine ourselves in the observed situation and assume the observed agent is behaving as we would) and have an interest in uncovering the "heuristic" (i.e., probability distribution) that best corresponds to human-like reasoning. Thus although, like us, they build on the formula at the centre of Ramirez and Geffner's non-probabilistic model (2009), which (effectively) subtracts the optimal cost of a plan from the optimal cost given the observations already seen, they use an alternative formula to derive the probability distribution across goals which takes the ratio of those two terms: an heuristic known to be a good match with human goal reasoning (Bonchek-Dokow & Kaminka, 2014).…”

Section: Plan Recognition As Planningmentioning

confidence: 99%

Cost-Based Goal Recognition in Navigational Domains

Masters¹,

Sardiña²

2019

jair

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Goal recognition is the problem of determining an agent's intent by observing her behaviour. Contemporary solutions for general task-planning relate the probability of a goal to the cost of reaching it. We adapt this approach to goal recognition in the strict context of path-planning. We show (1) that a simpler formula provides an identical result to current state-of-the-art in less than half the time under all but one set of conditions. Further, we prove (2) that the probability distribution based on this technique is independent of an agent's past behaviour and present a revised formula that achieves goal recognition by reference to the agent's starting point and current location only. Building on this, we demonstrate (3) that a Radius of Maximum Probability (i.e., the distance from a goal within which that goal is guaranteed to be the most probable) can be calculated from relative cost-distances between the candidate goals and a start location, without needing to calculate any actual probabilities. In this extended version of earlier work, we generalise our framework to the continuous domain and discuss our results, including the conditions under which our findings can be generalised back to goal recognition in general task-planning.

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“…The modeling of cognitive neuro-functions and their integration into computing platforms have given rise of machine intelligence having closer approximation to biological systems [2,5,7,8]. There can be a wide array of applications of such systems in the domains of artificial intelligence, humanoid robotics, self-adaptive distributed computing and, intelligent human-computer interactions.…”

Section: Motivationmentioning

confidence: 99%

“…Researchers have proposed models to predict intention based on pattern matching and sequence analysis [5]. The simulation theory is proposed by researchers to explain intention prediction [18].…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Distributed Computation using Evolutionary Consciousness : An Approach

Bagchi¹

2015

IJCIS

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The modeling of biological phenomena and its adaptations to distributed computing are promising research areas. The computational modeling of neurobiological phenomena, such as cognition and consciousness, has potential for applications into bio-inspired distributed computing. The functioning of neurological structures is inherently distributed in nature having a closer match to distributed computing. This paper proposes a mathematical model of state of consciousness by following the functional neurophysiology as well as elements of distributed computing. The scopes of evolution of consciousness and memory are incorporated into the model. The nodal classifications and formation of structural hierarchy in distributed computing nodes by incorporating elements of cognitive model are investigated. Evaluation of the model is made by numerical simulation considering different choice functions. The results illustrate that, model of consciousness can be adapted to bio-inspired distributed computing structures and the gradual evolution of consciousness is deterministic under fair excitations from environment.

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Towards computational models of intention detection and intention prediction

Cited by 18 publications

References 52 publications

Positive Affect Relevant to Epistemic Curiosity to Reflect Continuance Intention to Join a Hands-On Making Contest

Positive Affect Relevant to Epistemic Curiosity to Reflect Continuance Intention to Join a Hands-On Making Contest

Cost-Based Goal Recognition in Navigational Domains

Distributed Computation using Evolutionary Consciousness : An Approach

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