1,721,049 research outputs found
Proactive Persistent Agents - Using Situational Intelligence to Create Support Characters in Character-Centric Computer Games
No full textThroughout the 1990\u27s computer game development was dominated by the improvement of game graphics. However, graphical excellence is now the norm rather than the exception, and so other technologies are coming to the fore as developers strive to make their games stand out in an ever more crowded market. Sophisticated artificial intelligence (AI) is one such technology which is now receiving a large share of game development effort as game designers attempt to create more believable computer controlled characters.Influenced by the notion of situational intelligence, this work presents the proactive persistent agent (PPA) architecture, an intelligent agent architecture that has been developed to drive the behaviours of non player support characters in character centric computer games. The key features of the architecture are that: PPA characters display believable behaviour across a diverse range of situations, through the use of role passing and fuzzy cognitive maps; PPA based characters are capable of sophisticated social behaviours based on psychological models of personality, mood and interpersonal relationships; The architecture performs in real time on machines of modest specifications; It is relatively easy for non programmers to author characters behaviours using the architecture.The PPA architecture has been used in two demonstration applications (an adventure game and a system for animating sophisticated virtual humans) which will also be presented. These applications have been used to perform a rigorous evaluation of the architecture, which will also be described. Evaluation is notoriously difficult in systems used to simulate the behaviour of virtual characters, as the key notion of believability is troublingly subjective. However, in spite of the difficulties involved, an evaluation scheme has been developed and used. The evaluation shows that the PPA architecture is ideal for the contol of non player support characters in character centric games
Practical Work with Report 2
Get PDFDevelop a solution to a robotics challenge and write a short report about it
A Proposal for an Agent Architecture for Proactive Persistent Non Player Characters
No full textTCD-CS-2001-20In the past, games development has been driven by the need to achieve more realistic graphics.
This has resulted in a situation today with games set in visually stunning, and almost photo
realistic worlds being the norm. As game developers seek something new to make their games
stand out from the rest, artificial intelligence is playing a far more important role in game
design than before. The most obvious application of AI to games is in creating Non Player
Characters (NPCs). Although visually stunning worlds have been created for computer games,
the NPCs inhabiting these worlds are most often shallow and uninteresting. This paper will
introduce the idea of Proactive Persistent NPCs, that is NPCs that are always modelled (at least
to some extent) even when the human player is not in their vicinity. We will consider the issues
involved in creating these persistent characters, and propose an intelligent agent architecture
which will allow the creation of realistic, interesting and robust characters
Bluetooth Assassin: A Location-Based Game For Mobile Devices
Get PDFThis short paper will describe Bluetooth Assassin, a location-based game developed for mobile devices
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter
Get PDFDespite the success of ensemble classification methods in multi-class
classification problems, ensemble methods based on approaches other than
bagging have not been widely explored for multi-label classification problems.
The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that
exploits the sensor fusion properties of the Kalman filter to combine several
classifier models, and that has been shown to be very effective. This work
proposes a multi-label version of KFHE, ML-KFHE, demonstrating the
effectiveness of the KFHE method on multi-label datasets. Two variants are
introduced based on the underlying component classifier algorithm,
ML-KFHE-HOMER, and ML-KFHE-CC which uses HOMER and Classifier Chain (CC) as the
underlying multi-label algorithms respectively. ML-KFHE-HOMER and ML-KFHE-CC
sequentially train multiple HOMER and CC multi-label classifiers and aggregate
their outputs using the sensor fusion properties of the Kalman filter.
Extensive experiments and detailed analysis were performed on thirteen
multi-label datasets and eight other algorithms, which included
state-of-the-art ensemble methods. The results show, for both versions, the
ML-KFHE framework improves the predictive performance significantly with
respect to bagged combinations of HOMER (named E-HOMER), also introduced in
this paper, and bagged combination of CC, Ensemble Classifier Chains (ECC),
thus demonstrating the effectiveness of ML-KFHE. Also, the ML-KFHE-HOMER
variant was found to perform consistently and significantly better than the
compared multi-label methods including existing approaches based on ensembles
Interface Design Requirements For Playing Pong With A Single Switch Device
Get PDFMotivated by a desire for increased accessibility in digital games, in this paper we consider the design requirements for an interface to a variation of the game Pong for single-switch users. We consider the issues in the design of accessible interfaces for games and propose a set of interface configurations for playing Pong, using this as a proof of concept for more elaborate games
Employee engagement with a digital wellbeing platform:Insights from association rule mining analysis
Get PDFThe increasing availability of mental health data presents both opportunities and challenges, particularly due to the unstructured and noisy nature of such data. Data mining—an analytical approach for extracting knowledge from large datasets—is becoming increasingly prevalent in the fields of medicine and mental health. By employing data mining techniques, the insights can help inform the development of enhanced digital tools for mental health and fostering a more personalised user experience. One notable method within this domain is association rule mining, which identifies frequent relationships between items in a dataset. This study aims to apply association rule mining to a dataset generated by users of a digital employee wellbeing platform, focusing on the relationships between various tools and resources utilised on the platform. The Inspire Support Hub is a digital employee wellbeing platform featuring tools such as a mood tracker, a chatbot for self-assessments, and psychoeducational resources. User interactions with the platform are logged as anonymous events, including clicks, mood entries, and self-assessment results, each associated with a unique user ID and timestamp. Upon registration, users enter a company pin and their sector is recorded. From February 2019 to April 2023, 11,583 users engaged with the platform over 16,657 sessions. The analysis was conducted using R Studio, employing the dplyr and tidyverse packages for data cleaning and wrangling, along with ggplot2 for visualisation. The event logs were transformed into transaction data for association rule mining using the arules package. The Apriori algorithm was applied with a minimum support threshold of 0.05 and a confidence level of 0.8, ensuring that only rules with at least 80% accuracy were included. Applying association rule mining on the employee wellbeing platform dataset revealed distinct sets of co-associations, with significant emphasis on the chatbot and mood tracker. This is predictable, considering that the iHelpr chatbot, anxiety and stress self-assessments, and mood tracker are the platform’s most frequently used components. The association rules derived from this analysis can offer valuable insights into the user journey, such as discovering frequent usage patterns, and based upon this the platform could recommend personalised features and content tailored to each user's preferences and behaviour
Employee engagement with a digital wellbeing platform:Insights from association rule mining analysis
Get PDFThe increasing availability of mental health data presents both opportunities and challenges, particularly due to the unstructured and noisy nature of such data. Data mining—an analytical approach for extracting knowledge from large datasets—is becoming increasingly prevalent in the fields of medicine and mental health. By employing data mining techniques, the insights can help inform the development of enhanced digital tools for mental health and fostering a more personalised user experience. One notable method within this domain is association rule mining, which identifies frequent relationships between items in a dataset. This study aims to apply association rule mining to a dataset generated by users of a digital employee wellbeing platform, focusing on the relationships between various tools and resources utilised on the platform. The Inspire Support Hub is a digital employee wellbeing platform featuring tools such as a mood tracker, a chatbot for self-assessments, and psychoeducational resources. User interactions with the platform are logged as anonymous events, including clicks, mood entries, and self-assessment results, each associated with a unique user ID and timestamp. Upon registration, users enter a company pin and their sector is recorded. From February 2019 to April 2023, 11,583 users engaged with the platform over 16,657 sessions. The analysis was conducted using R Studio, employing the dplyr and tidyverse packages for data cleaning and wrangling, along with ggplot2 for visualisation. The event logs were transformed into transaction data for association rule mining using the arules package. The Apriori algorithm was applied with a minimum support threshold of 0.05 and a confidence level of 0.8, ensuring that only rules with at least 80% accuracy were included. Applying association rule mining on the employee wellbeing platform dataset revealed distinct sets of co-associations, with significant emphasis on the chatbot and mood tracker. This is predictable, considering that the iHelpr chatbot, anxiety and stress self-assessments, and mood tracker are the platform’s most frequently used components. The association rules derived from this analysis can offer valuable insights into the user journey, such as discovering frequent usage patterns, and based upon this the platform could recommend personalised features and content tailored to each user's preferences and behaviour
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