The task of supporting a human operator to understand generated plans, and to explore the plan space, are important problems in automated planning. In this work, we consider the problem of plan explainability and plan space exploration in underwater autonomous vehicle missions. In this context, concepts that are useful for querying the system, such as distance and duration, will not necessarily map directly onto components of the planning model, such as actions. To overcome this difficulty, we focus on an important substructure of these problems: the multi-agent spatial-temporal (MAST) structure. Using this structure, we define a collection of model extensions, which include additional concepts relevant to the MAST structure. We then consider the problem of user-guided plan space exploration, and identify useful query types in this domain, including user queries based on numeric functions. These queries can make use of the extended model, allowing the user to directly reference the new concepts. In an empirical study, we demonstrate the use of the new structure within queries, and compare the new query types in our target domain, and in benchmark domains with the MAST structure. Finally, we report on a qualitative user study, where we investigate the use of these new structural concepts in underwater autonomous vehicle scenarios. Our study indicates that the extended concepts can be used in user queries and agent responses, enabling the user to better communicate their intent in shaping mission objectives, and supporting explanations with more relevant information.

In autonomous vehicle mission planning, supporting human operators to understand and influence the decision-making process is crucial for building the operator’s trust and establishing effective collaboration. However, it has been observed that human and agent representations will typically not align. As a consequence, concepts that are useful for effective human-agent communication, will not necessarily feature in the agent’s representation. Focusing on specific spatial-temporal concepts, we define automatic model extensions, which can introduce these additional concepts. We report on a qualitative user study, where we investigate the use of these new structural concepts in underwater autonomous vehicle scenarios. Our study indicates that the extended concepts can be used in user queries and agent responses, enabling the user to better communicate their intent in shaping mission objectives, and supporting explanations with more relevant information.

This work analyses the relationship between the way robots gesture and the way those gestures are perceived by human users. In particular, this work shows how modifying the amplitude and speed of a gesture affect the Godspeed scores given to those gestures, by means of an experiment involving 45 stimuli and 30 observers. The results suggest that shaping gestures aimed at manifesting the inner state of the robot (e.g., cheering or showing disappointment) tends to change the perception of Animacy (the dimension that accounts for how driven by endogenous factors the robot is perceived to be), while shaping gestures aimed at achieving an interaction effect (e.g., engaging and disengaging) tends to change the perception of Anthropomorphism, Likeability and Perceived Safety (the dimensions that account for the social aspects of the perception).

This work investigates the role of the attraction paradigm - the tendency to associate similarity and attraction in interpersonal relations - in Human-Robot Interaction. The experiment presented here involved 30 human observers who watched and rated 45 robotic gestures in terms of Big-Five personality traits and Godspeed scores. The results show that, for 24 of the 30 observers, there was a statistically significant correlation between the Godspeed scores and the perceived similarity between the robot's personality and their own. However, the association was positive for 15 subjects - meaning that for these there is a similarity-attraction effect - and negative for the other 9 - meaning that for these there is a complementarity-attraction effect. Furthermore, the strength of the effect depends on the particular trait under examination.

This work explores the role of personality as a mediation variable between the observable behaviour of a robot - gestures of different energy and spatial extension in the experiments of this work - and the subjective experience of its users as measure by the Godspeed questionnaire. The results show that, at least for some traits, the Big Five personality traits that the users attribute to a robot are predictive of the Godspeed scores, i.e., of the quality of the interaction the users have with the robot. In other words, robots that are attributed different personality traits tend to be perceived differently in relation to the quality of the interaction.

This work investigates the relationship between the perception that people develop about a robot and the understandability of the gestures the latter displays. The experiments have involved 30 human observers that have rated 45 robotic gestures in terms of the Godspeed dimensions. At the same time, the observers have assigned a score to 10 possible interpretations (the same interpretations for all gestures). The results show that there is a statistically significant correlation between the understandability of the gestures - measured through an information theoretic approach - and all Godspeed scores. However, the correlation is positive in some cases (Anthropomorphism, Animacy and Perceived Intelligence), but negative in others (Perceived Safety and Likeability). In other words, higher understandability is not necessarily associated with more positive perceptions.

In this demonstration we present a repertoire of social signals generated by the humanoid robot Pepper in the context of the EU-funded project MuMMER. The aim of this research is to provide the robot with the expressive capabilities required to interact with people in real-world public spaces such as shopping malls-and being able to control the non-verbal behaviour of such a robot is key to engaging with humans in an effective way. We propose an approach to modulating the non-verbal social signals of the robot based on systematically varying the amplitude and speed of the joint motions and gathering user evaluations of the resulting gestures. We anticipate that the humans' perception of the robot behaviour will be influenced by these modulations

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.

Digital Humanities offer a new exciting domain for agent-based distributed simulation. In historical studies interpretation rarely rises above the level of unproven assertion and is rarely tested against a range of evidence. Agent-based simulation can provide an opportunity to break these cycles of academic claim and counterclaim. The MWGrid framework utilises distributed agent based simulation to study medieval military logistics. As a use-case, it has focused on the logistical analysis of the Byzantine army's march to the battle of Manzikert (AD 1071), a key event in medieval history. It integrates an agent design template, a transparent, layered mechanism to translate model-level agents' actions to time stamped events and the PDES-MAS distributed simulation kernel. The paper presents an overview of the MWGrid system and a quantitative evaluation of its performance.

Groundwater is like dark matter - we know very little apart from the fact that it is hugely important. Given the scarcity of data, mathematical modelling can come to the rescue but existing groundwater models are mainly restricted to simulate the transport and degradation of contaminants on the scale of whole contaminated field sites by averaging out the effect of spatial heterogeneity on the availability of the pollutant to the degrading organisms. These coarse-scale mean-field models therefore tend to rely on fitting to data rather than being predictive. Also, they are less suited to incorporate spatial variability and non-linear kinetics and feedbacks. We propose to solve the two mutually exacerbating problems of environmental patchiness and data scarcity by developing a flexible and robust distributed simulation framework that uses an ensemble of small scale simulations running on different processors/computers to scale-up, i.e. to feed the effect of small-scale patchiness into a concurrent site-scale simulation of the dynamics of groundwater pollutant degradation. Our scaling approach solves problem #1 by simulating dynamics also on the small scale where some of the patchiness resides, and problem #2 by enabling rigorous validation of our small-scale model and scaling approach with laboratory data, which are high quality at low cost.

Range-Query is an important associative form of data access in distributed simulations and Distributed Virtual Environments. This paper discusses the problem of Range-Queries in the context of distributed simulation of multi-agent systems. An algorithm is presented for performing instantaneous Queries within an optimistic synchronisation framework and in the presence of dynamic migration of the simulation state. A quantitative evaluation of the effectiveness of the algorithm under different conditions is also presented.

We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their "de-evolutionarised" variants. The experiments show that "de-evolutionarising" can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the "memetic niche", it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its "de-evolutionarised" variant.

Constraint handling is not straightforward in evolutionary algorithms (EAs) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade, numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the evolutionary computing field.

Evolutionary approaches to constraint satisfaction problems (CSPs) are often tested on a set of randomly generated instances. Recently it has been shown that the frequently used random problem instance generators are not good enough. This implies that conclusions based on the usage of these generators need to be revised. In this paper, we perform an extensive experimental comparison of CSP solving EAs based on a new, improved generator. In particular, we compare two representations, two evaluation functions and EAs with and without the so-called SAWing mechanism, each of these with different population sizes and variation operators. The resulting systematic overview confirms some "myths", while refutes others. Most notably, the role of the population size and that of SAWing itself turn out to be different from what is usually assumed.

Evolutionary algorithms (EAs) for solving constraint satisfaction problems (CSPs) can be roughly divided into two classes: EAs with adaptive fitness functions and heuristic-based EAs. A.E. Eiben et al. (1998) compared effective EAs of the first class experimentally using a large set of benchmark instances consisting of randomly-generated binary CSPs. In this paper, we complete this comparison by performing the same experiments using three of the most effective heuristic-based EAs. The results of our experiments indicate that the three heuristic-based EAs have similar performances on random binary CSPs. Comparing these results with those of A.E. Eiben et al., we are able to identify the best EA for binary CSPs as the algorithm introduced by G. Dozier et al. (1994), which uses a heuristic as well as an adaptive fitness function.