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Trust is a critical issue in human–robot interactions: as robotic systems gain complexity, it becomes crucial for them to be able to blend into our society by maximizing their acceptability and reliability. Various studies have examined how trust is attributed by people to robots, but fewer have investigated the opposite scenario, where a robot is the trustor and a human is the trustee. The ability for an agent to evaluate the trustworthiness of its sources of information is particularly useful in joint task situations where people and robots must collaborate to reach shared goals. We propose an artificial cognitive architecture based on the developmental robotics paradigm that can estimate the trustworthiness of its human interactors for the purpose of decision making. This is accomplished using Theory of Mind (ToM), the psychological ability to assign to others beliefs and intentions that can differ from one’s owns. Our work is focused on a humanoid robot cognitive architecture that integrates a probabilistic ToM and trust model supported by an episodic memory system. We tested our architecture on an established developmental psychological experiment, achieving the same results obtained by children, thus demonstrating a new method to enhance the quality of human and robot collaborations. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Tumour Treating Fields (TTFields) are a regional, antimitotic treatment for solid tumours, which is based on the delivery of low-intensity alternating electric fields. The aim of the STELLAR study was to test the activity of TTFields delivered to the thorax in combination with systemic chemotherapy for the front-line treatment of patients with unresectable malignant pleural mesothelioma. STELLAR was a prospective, single-arm, phase 2 trial done at 12 European academic and non-academic sites (five in Italy, three in Poland, one in France, one in Belgium, one in Spain, and one in the Netherlands) for treatment-naive patients with histologically confirmed unresectable malignant pleural mesothelioma. Patients were aged at least 18 years, had an Eastern Cooperative Oncology Group performance status of 0–1, and at least one measurable or evaluable lesion according to modified Response Evaluation Criteria in Solid Tumors for mesothelioma. Patients received continuous TTFields at a frequency of 150 kHz to the thorax and concomitant chemotherapy with intravenous pemetrexed (500 mg/m2 on day 1) plus intravenous platinum (either cisplatin 75 mg/m2 on day 1 or carboplatin area under the curve 5 on day 1) every 21 days for up to six cycles. Patients not progressing after completion of chemotherapy received TTFields as maintenance treatment until progression, patient or physician decision, or unacceptable toxic effects. The primary endpoint of the trial was overall survival. Survival analyses were done in the intention-to-treat population, and safety analyses were done in all patients who received at least 1 day of TTFields treatment. This trial is registered with ClinicalTrials.gov, NCT02397928. Between Feb 9, 2015 and March 21, 2017, 80 patients were enrolled in the study. Median follow-up was 12·5 months (IQR 7·4–16·6). Median overall survival was 18·2 months (95% CI 12·1–25·8). The most common grade 3 or worse adverse events were anaemia (nine [11%] patients), neutropenia (seven [9%]), and thrombocytopenia (four [5%]). Skin reaction was the only adverse event associated with TTFields and was reported as grade 1–2 in 53 (66%) patients, and as grade 3 in four (5%) patients. No treatment-related deaths were observed. The trial showed encouraging overall survival results, with no increase in systemic toxicity. TTFields (150 kHz) delivered to the thorax concomitant with pemetrexed and platinum was an active and safe combination for front-line treatment of unresectable malignant pleural mesothelioma. Further investigation in a randomised trial is warranted. Novocure.

As Artificial Intelligence and Robotics evolve, the ethical implications of autonomous systems are becoming increasingly paramount. This article explores the role of a robot’s inner speech in enhancing human phronesis - the capacity for making ethical and contextually appropriate decisions. Phronesis is a complex human trait based on experience, personality, and values, and is crucial for decisions affecting others’ well-being. While artificial systems (i.e., robots) may not fully replicate human practical wisdom, they can support humans by stimulating reflection and helping in situations requiring careful consideration. This support can be more impactful if the robot has the ability for inner speech. The article examines whether a robot engaging in internal dialogue can help develop phronesis in people interacting with it, especially in contexts where caring of people with special needs is crucial. A group of participants was involved in a virtual game, where they had to care for a suffering person alongside a robot. Their decisions, in terms of task resolution and ethical considerations, allowed to assess if the robot’s inner speech increased participants’ awareness of the suffering person’s needs. Preliminary results are promising, highlighting the potential of thinking robots to assist in making ethical decisions.

Assessment of EGFR mutation in non-small cell lung cancer (NSCLC) patients is mandatory for optimization of pharmacologic treatment. In this respect, mutation analysis of circulating tumor cells (CTCs) may be desirable since they may provide real-time information on patient's disease status. Blood samples were collected from 37 patients enrolled in the TRIGGER study, a prospective phase II multi-center trial of erlotinib treatment in advanced NSCLC patients with activating EGFR mutations in tumor tissue. 10 CTC preparations from breast cancer patients without EGFR mutations in their primary tumors and 12 blood samples from healthy subjects were analyzed as negative controls. CTC preparations, obtained by the Veridex CellSearch System, were subjected to ultra-deep next generation sequencing (NGS) on the Roche 454 GS junior platform. CTCs fulfilling all Veridex criteria were present in 41% of the patients examined, ranging in number between 1 and 29. In addition to validated CTCs, potential neoplastic elements were seen in 33 cases. These included cells not fulfilling all Veridex criteria (also known as \"suspicious objects\") found in 5 (13%) of 37 cases, and isolated or clustered large naked nuclei with irregular shape observed in 33 (89%) cases. EGFR mutations were identified by NGS in CTC preparations of 31 (84%) patients, corresponding to those present in matching tumor tissue. Twenty-five (96%) of 26 deletions at exon 19 and 6 (55%) of 11 mutations at exon 21 were detectable (P = 0.005). In 4 (13%) cases, multiple EGFR mutations, suggesting CTC heterogeneity, were documented. No mutations were found in control samples. We report for the first time that the CellSearch System coupled with NGS is a very sensitive and specific diagnostic tool for EGFR mutation analysis in CTC preparations with potential clinical impact.

Intelligent systems operating in real-world environments are often required to make decisions in contexts that are only partially known at design time. In such scenarios, the assumption of a static and fully specified knowledge base becomes unrealistic, limiting the system’s ability to adapt to novel situations. This challenge is particularly relevant for robotic systems, whose behavior cannot be entirely pre-programmed when operating in dynamic and evolving environments. This paper proposes a methodological and architectural approach for the runtime update of ontologies and knowledge bases, enabling intelligent systems to autonomously adapt their internal representation of the world during execution. The proposed approach enables the system to identify knowledge gaps by distinguishing between previously unknown concepts and known concepts enriched with newly observed instances, and to integrate such information into the ontology in a controlled and consistent manner. The approach is implemented as an end-to-end pipeline that combines visual perception, semantic interpretation through large language models, and a robust ontology update mechanism. Particular attention is devoted to ensuring formal consistency during runtime evolution, addressing challenges such as the generation of valid OWL constructs, the management of inverse properties, datatype normalization, and the prevention of semantic degradation over iterative updates. By enabling knowledge-driven adaptation at runtime, the proposed framework supports autonomous decision-making in environments that cannot be fully anticipated at design time. The approach was developed within the MUSIC4D and MHARA projects, which explore the use of intelligent systems in dynamic, partially structured contexts, focusing on knowledge-based adaptation.

The swarms of robots are examples of artificial collective intelligence, with simple individual autonomous behavior and emerging swarm effect to accomplish even complex tasks. Modeling approaches for robotic swarm development is one of the main challenges in this field of research. Here, we present a robot-instantiated theoretical framework and a quantitative worked-out example. Aiming to build up a general model, we first sketch a diagrammatic classification of swarms relating ideal swarms to existing implementations, inspired by category theory. Then, we propose a matrix representation to relate local and global behaviors in a swarm, with diagonal sub-matrices describing individual features and off-diagonal sub-matrices as pairwise interaction terms. Thus, we attempt to shape the structure of such an interaction term, using language and tools of quantum computing for a quantitative simulation of a toy model. We choose quantum computing because of its computational efficiency. This case study can shed light on potentialities of quantum computing in the realm of swarm robotics, leaving room for progressive enrichment and refinement.

BackgroundCirculating cell-free DNA (cfDNA) may help understand the molecular response to pharmacologic treatment and provide information on dynamics of clonal heterogeneity. Therefore, this study evaluated the correlation between treatment outcome and activating EGFR mutations (act-EGFR) and T790M in cfDNA in patients with advanced NSCLC given osimertinib.MethodsThirty-four NSCLC patients resistant to first/second-generation EGFR-TKIs, positive for both act-EGFR and T790M in cfDNA at the time of progression were enrolled in this study. Plasma samples were obtained at osimertinib baseline and after 3 months of therapy; cfDNA was analyzed by droplet digital PCR and results were expressed as mutant allele frequency (MAF).ResultsAt baseline, act-EGFR MAF was significantly higher than T790M (p < 0.0001). act-EGFR MAF and T790M/act-EGFR MAF ratio were significantly correlated with disease response (p = 0.02). Cut-off values of act-EGFR MAF and T790M/act-EGFR ratio of 2.6% and 0.22 were found, respectively. The PFS of patients with act-EGFR MAF of > 2.6% and < 2.6%, were 10 months vs. not reached, respectively (p = 0.03), whereas patients with T790M/act-EGFR ≤ 0.22 had poorer PFS than patients with a value of > 0.22 (6 months vs. not reached, respectively, p = 0.01).Conclusionact-EGFR MAF and T790M/act-EGFR MAF ratio are potential markers of outcome in patients treated with osimertinib.

Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling. In the present study, locked-in ALS patients used a BCI system to directly control the humanoid robot NAO (Aldebaran Robotics, France) with the aim of reaching and grasping a glass of water. Four ALS patients and four healthy controls were recruited and trained to operate this humanoid robot through a P300-based BCI. A few minutes training was sufficient to efficiently operate the system in different environments. Three out of the four ALS patients and all controls successfully performed the task with a high level of accuracy. These results suggest that BCI-operated robots can be used by locked-in ALS patients as an artificial alter-ego, the machine being able to move, speak and act in his/her place.

The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot’s behavior. According to the production rules, the planning of the robot activities is processed in a recognize–act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up.

This study presents an approach for developing digital avatars replicating individuals’ physical characteristics and communicative style, contributing to research on virtual interactions in the metaverse. The proposed method integrates large language models (LLMs) with 3D avatar creation techniques, using what we call the Tree of Style (ToS) methodology to generate stylistically consistent and contextually appropriate responses. Linguistic analysis and personalized voice synthesis enhance conversational and auditory realism. The results suggest that ToS offers a practical alternative to fine-tuning for creating stylistically accurate responses while maintaining efficiency. This study outlines potential applications and acknowledges the need for further work on adaptability and ethical considerations.