TY  - GEN
AB  - Since their appearance, computer programs have embodied discipline and structured approaches and methodologies. Yet, to this day, equipping machines with imaginative and creative capabilities remains one of the most challenging and fascinating goals we pursue. Intelligent software agents can behave intelligently in well-defined scenarios, relying on Machine Learning (ML), symbolic reasoning, and the ability of their developers for tailoring smart behaviors to specific application domains. However, to forecast the evolution of all possible scenarios is unfeasible. Thus, intelligent agents should autonomously/creatively adapt to the world’s mutability. This paper investigates the meaning of imagination in the context of cognitive agents. In particular, it addresses techniques and approaches to let agents autonomously imagine/simulate their course of action and generate explanations supporting it, and formalizes thematic challenges. Accordingly, we investigate research areas including: (i) reasoning and automatic theorem proving to synthesize novel knowledge via inference; (ii) automatic planning and simulation, used to speculate over alternative courses of action; (iii) machine learning and data mining, exploited to induce new knowledge from experience; and (iv) biochemical coordination, which keeps imagination dynamic by continuously reorganizing it.
AD  - University of Bologna, Cesena, Italy
AD  - University of Luxembourg, Esch-sur-Alzette, Luxembourg
AD  - University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
AD  - University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
AU  - Ciatto, Giovanni
AU  - Najjar, Amro
AU  - Calbimonte, Jean-Paul
AU  - Calvaresi, Davide
CY  - London, UK
DA  - 2021-05
DO  - 10.1007/978-3-030-82017-6_9
DO  - DOI
ID  - 9059
JF  - Explainable and Transparent AI and Multi-Agent Systems : Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers
KW  - Informatique
KW  - multi-agent systems
KW  - imagination
KW  - BDI
KW  - cognitive agents
KW  - XAI
L1  - https://arodes.hes-so.ch/record/9059/files/Author%20postprint.pdf
L2  - https://arodes.hes-so.ch/record/9059/files/Author%20postprint.pdf
L4  - https://arodes.hes-so.ch/record/9059/files/Author%20postprint.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/9059/files/Author%20postprint.pdf
N2  - Since their appearance, computer programs have embodied discipline and structured approaches and methodologies. Yet, to this day, equipping machines with imaginative and creative capabilities remains one of the most challenging and fascinating goals we pursue. Intelligent software agents can behave intelligently in well-defined scenarios, relying on Machine Learning (ML), symbolic reasoning, and the ability of their developers for tailoring smart behaviors to specific application domains. However, to forecast the evolution of all possible scenarios is unfeasible. Thus, intelligent agents should autonomously/creatively adapt to the world’s mutability. This paper investigates the meaning of imagination in the context of cognitive agents. In particular, it addresses techniques and approaches to let agents autonomously imagine/simulate their course of action and generate explanations supporting it, and formalizes thematic challenges. Accordingly, we investigate research areas including: (i) reasoning and automatic theorem proving to synthesize novel knowledge via inference; (ii) automatic planning and simulation, used to speculate over alternative courses of action; (iii) machine learning and data mining, exploited to induce new knowledge from experience; and (iv) biochemical coordination, which keeps imagination dynamic by continuously reorganizing it.
PB  - 3-7 May 2021
PP  - London, UK
PY  - 2021-05
SN  - 978-3-030-82016-9
SN  - 0302-9743
T1  - Towards explainable visionary agents :license to dare and imagine
TI  - Towards explainable visionary agents :license to dare and imagine
UR  - https://arodes.hes-so.ch/record/9059/files/Author%20postprint.pdf
VL  - Lecture Notes in Computer Science, vol. 12688
Y1  - 2021-05
ER  -