Anzahl der Treffer: 162
Erstellt: Thu, 20 Jun 2024 23:20:29 +0200 in 0.0700 sec

Arnold, Oksana; Franke, Ronny; Jantke, Klaus P.; Knauf, Rainer; Schramm, Tanja; Wache, Hans-Holger
Deontic knowledge representation and reasoning in industrial accident prevention training by means of time travel prevention games. - In: International journal of advanced corporate learning, ISSN 1867-5565, Bd. 17 (2024), 2, S. 4-16

Industrial accident prevention is an issue of societal relevance to avoid loss of human lives, injuries, damage of installations, and financial losses. The authors deploy game-based training in virtual environments where trainees experience challenges of safe operation and disastrous self-induced accidents. Nothing is more affective and, thus, effective than a trainee’s own experience. Time travel prevention games are a game category particularly tailored to the needs of human players who look for opportunities to make good for a damage. Time travel pre-vention games for purposes such as accident prevention in the industries are ad-vantageous due to their conservation of resources including human health and lives. They are affective by allowing for unprecedented learner/player/trainee ex-periences and they are effective due to the fascination of application-oriented game play including opportunities to influence the fate, the latter being less close to reality, but the more attractive and worth telling. For optimal guidance to human trainees, the digital game system needs to learn about the trainees’ strength and weaknesses, about needs and desires. In terms of behavioral sciences, the system observing a human’s behavior hypothesizes theories of mind. In training games, modalities of events/actions are decisive. There are modalities of events/actions such as possibility, unavoidability, and the like as well as obliga-tions and oughts. Training aims at the emergence of cognitive states that are use-ful in practice. The system’s reasoning is deontic.
Ikegami, Yukino; Tsuruta, Setsuo; Kutics, Andrea; Damiani, Ernesto; Knauf, Rainer
Fast ML-based next-word prediction for hybrid languages. - In: Internet of things and cyber-physical systems, ISSN 2667-3452, Bd. 25 (2024), 101064, S. 1-15

Smartphone users are beyond two billion worldwide. Heavy users of the texting application rely on input prediction to reduce typing effort. In languages based on the Roman alphabet, many techniques are available. However, Japanese text is based on multiple character sets such as Kanji (Chinese-like word symbols), Hiragana and Katakana syllable sets. For its time/labor intensive input, next word prediction is crucial. It is still an open challenge. To tackle this, a hybrid language model is proposed. It integrates a Recurrent Neural Network (RNN) with an n-gram model. RNNs are powerful models for learning long sequences for next word prediction. N-gram models are best at current word completion. Our RNN language model (RNN-LM) predicts the next words. According the “price” of the performance gain paid by a higher time complexity, our model best deploys on a client-server architecture. Heavily-loaded RNN-LM deploys on the server while the n-gram model on the client. Our RNN-LM consists of an input layer equipped with word embedding, an output layer, and hidden layers connected with LSTMs (Long Short-Term Memories). Training is done via BPTT (Back Propagation Through Time). For robust training, BPTT is elaborated by learning rate refinement and gradient norm scaling. To avoid overfitting, the dropout technique is applied except for LSTM. Our novel model is compact (2 LSTMs, 650 units per layer), indeed. Due to synergetic elaboration, it shows 10 % lower perplexity than Zaremba's excellent conventional models in our Japanese text prediction experiment. Our model has been incorporated into IME (Input Method Editor) we call Flick. On the Japanese text input experiment, Flick outperforms Mozc (Google Japanese Input) by 16 % in time and 34 % in the number of keystrokes.
Gonzalez, Avelino J.; Anchor, Thomas; Hevia, Anthony; Posadas, Andres; Wade, Josh; Ansag, Rebecca A.; Benko, Kyle; Bottoni, Brooke; Kazakova, Vera; Alvarez, Matthew J.; Wong, Josiah M.; Martin, Jordan; Knauf, Rainer; Jantke, Klaus P.; Wu, Annie S.
The evolution of the fAIble system to automatically compose and narrate stories for children. - In: Journal of experimental & theoretical artificial intelligence, ISSN 1362-3079, Bd. 36 (2024), 4, S. 611-656

This article describes our long-term research into automated story generation and our resulting story generation architecture called fAIble that incorporates several innovations. fAIble determines each event that occurs in the tale using a combination of scripted sequences and stochastically chosen events. The probability of an event occurring is based on the skills and personalities of the characters who have agency. Event selection is also influenced by the context of the situation faced by the characters. Each event is associated with a description in grammatically-correct natural language that can be narrated orally via text-to-speech. We describe the evolution of fAIble, its architecture and the results of our independent evaluation of each of the four progressively developed fAIble prototypes (fAIble 0, I, II and III), as tested with human test subjects. On a continuous scale where 0 means unacceptable, 1 means acceptable and 2 means optimal, the composite human test subject rating average from the independent tests of the prototypes was 0.933. The paper also describes a summative assessment where test subjects were asked to review stories from all four prototypes and rank them comparatively. These comparative results indicate an improvement from the original (fAIble 0) to the last one (fAIble III).
Arnold, Oksana; Franke, Ronny; Jantke, Klaus P.; Knauf, Rainer; Schramm, Tanja; Wache, Hans-Holger
Thinking and chatting deontically - novel support of communication for learning and training with time travel prevention games. - In: Creative approaches to technology-enhanced learning for the workplace and higher education, (2023), S. 25-37

The authors’ key area of application is training for the prevention of accidents in the process technology industries. They run a professional training center with own 3D virtual environments. At TLIC 2021, four of the present authors delivered a contribution advocating planning of human training experiences as dynamically as managing some severely disturbed technical system back into a normal operation - such as an out of control chemical reactor - and enabling human trainees who failed to complete a risky task - thereby possibly ruining a (fortunately only virtual) technical installation - to virtually travel back in time to make good the damage. At TLIC 2022, they introduced cascades of gradually more intricate categories of time travel games. With every step from one category to the next, the deployed AI gets more powerful and effective in providing adaptive guidance of human trainees. The most advanced time travel games are those that allow for the dynamic modification of events experienced in the virtual past. In this way, the game system evolves over time and adapts to the needs of human trainees with emphasis on guidance for trainees who fail repeatedly. The extended team of authors presents a novel perspective at time travel prevention games that leads to a more human-centered adaptive guidance. Training is seen through the lens of deontic modal logic. The focus is on undesired events such as explosions, fire, health hazards due to toxic vapors, and the like. The game system’s AI is reasoning about necessity and possibility of such events. It offers to human trainees/players helpful chats about modalities of decisive events of training.
Schramm, Tanja; Knauf, Rainer
A project to compose a modular AI certification system in university education and its inherent chance to verify, validate, and refine AI teaching by AI technologies. - In: Proceedings of FLAIRS-36, May 14-17, 2023, Clearwater Beach, FL, (2023), insges. 6 S.

A current project of the German Federal State of Thuringia aims at bundling the various AI teaching activities of the involved universities that includes besides technological also social issues. On their way to meet the project objectives, the authors aim at utilizing such unique opportunity to consider the various successful experiences in teaching several AI content issues of the project members to revisit a formerly developed concept of semi-formally representing didactic knowledge and making it a subject of Knowledge Engineering technologies such as consistency issues as well as chances to validate learning paths and refine them based on the validation results. Ideas towards this objective and first results are sketched in this paper.
Bottoni, Brooke; Moolenaar, Yasmine; Hevia, Anthony; Anchor, Thomas; Benko, Kyle; Knauf, Rainer; Jantke, Klaus P.; Gonzalez, Avelino J.; Wu, Annie
Character depth and sentence diversification in automated narrative generation. - In: Proceedings of the Thirty-third International Florida Artificial Intelligence Research Society Conference, (2020), S. 21-26

This paper describes and discusses methods for improving character depth and sentence diversification in automated storytelling systems. The fAIble III system that is the subject of this paper addresses a major limitation of its immediate predecessor (fAIble II) in that the characters in its stories seemed to act in a vacuum, without any apparent reasons for their choices or emotions. This is accomplished through generating character backstories. fAIble III also addresses the diversity of generated sentences with a pattern recognition system that removes many of the awkward and repetitive sentences that drew negative comments in the testing of fAIble I and II. Lastly, stories generated by fAIble II and fAIble III are compared and empirical test results are presented.

Sakurai, Eriko; Kurashige, Kentarou; Tsuruta, Setsuo; Sakurai, Yoshitaka; Knauf, Rainer; Damiani, Ernesto; Kutics, Andrea; Frati, Fulvio
Embodiment matters: toward culture-specific robotized counselling. - In: Journal of reliable intelligent environments, ISSN 2199-4676, Bd. 6 (2020), 3, S. 129-139

In this paper, we propose adding the traditional Japanese nodding behavior to the repertoire of social movements to be used in the context of human-robot interaction. Our approach is motivated by the notion that in many cultures, trust-building can be boosted by small body gestures. We discuss the integration of a robot capable of such movements within CRECA, our context-respectful counseling agent. The frequent nodding called "unazuki" in Japan, often accompanying the "un-un" sound (meaning "I agree") of Japanese onomatopoeia, underlines empathy and embodies unconditioned approval. We argue that unazuki creates more empathy and promotes longer conversation between the robotic counsellor and people. We set up an experiment involving ten subjects to verify these effects. Our quantitative evaluation is based on the classic metrics of utterance, adapted to support the Japanese language. Interactions featuring "unazuki" showed higher value of this metrics. Moreover, subjects assessed the counselling robot's trustworthiness and kindness as "very high" (Likert scale: 5.5 versus 3-4.5) showing the effect of social gestures in promoting empathetic dialogue to general people including the younger generation. Our findings support the importance of social movements when using robotized agents as a therapeutic tool aimed at improving emotional state and social interactions, with unambiguous evidence that embodiment can have a positive impact that warrants further exploration. The 3D printable design of our robot supports creating culture-specific libraries of social movements, adapting the gestural repertoire to different human cultures.
Batarseh, Feras A.; Gonzalez, Avelino J.; Knauf, Rainer
Context-assisted test cases reduction for cloud validation. - In: SSRN eLibrary, ISSN 1556-5068, (2020), insges. 14 S.
Last revised: 4 May 2020

Cloud computing is currently receiving much attention from the industry, government, and academia. It has changed the way computation is performed and how services are delivered to customers. Most importantly, cloud services change the way we design software, handle data, and perform testing. In cloud computing, testing is delivered as a service (TaaS). Case testing is one of the most common validation approaches for software. However, executing test cases on a software system could be expensive and time consuming. Therefore, test case reduction is performed to minimize the number of test cases to be executed on the system. In this paper, we introduce a validation method called Context-Assisted Test Case Reduction (CATCR) for systems that are deployed on the cloud. In CATCR, test cases are reduced based on the context of the validation process. The results of previous test cases are used to select the next set of test cases while the validation process is ongoing. The minimized set of test cases needs to have effective coverage of the entire system. To evaluate CATCR, an experimental evaluation is performed through Amazon's Cloud and a Java validation tool. Experimental results are recorded and presented.
Alvarez, Matthew J.; Amaya, Rebeca E.; Benko, Kyle A.; Martin, Jordan T.; Knauf, Rainer; Jantke, Klaus P.; Gonzalez, Avelino J.
Hello, narratives: character development in automated narrative generation. - In: Proceedings of the Thirty-second International Florida Artificial Intelligence Research Society Conference, (2019), S. 264-269

Development of interesting and complex characters is the most important element of a narrative. Presented in this work is fAIble II, an automated narrative generation system that focuses on character development. fAIble II leverages a graph database, containerized modules, knowledge templates, and language structuring to produce diverse and coherent stories. Story progression is driven by character perception, emotion, personality, and interaction with the story world. The resultant system has been tested via anonymous questionnaire. Responses suggest its ability to create diverse, sensible narratives using character development

Ikegami, Yukino; Knauf, Rainer; Damiani, Ernesto; Tsuruta, Setsuo; Sakurai, Yoshitaka; Sakurai, Eriko; Kutics, Andrea; Nakagawa, Akihiko
High performance personal adaptation speech recognition framework by incremental learning with plural language models. - In: The 15th International Conference on Signal Image Technology & Internet Based Systems, (2019), S. 470-474