The Influence of Social Norms and Social Consciousness on Intention Reconciliation

Research on resource-bounded agents has established that rational agents need to be able to revise their commitments in light of new opportunities. In the context of collaborative activities, rational agents must be able to reconcile their intentions to do team-related actions with other, conflicting intentions. The SPIRE experimental system allows the process of intention reconciliation in team contexts to be simulated and studied. Initial work with SPIRE examined the impact of environmental factors and agent utility functions on individual and group outcomes in the context of one set of social norms governing collaboration. This paper extends those results by further studying the effect of environmental factors and the agents' level of social consciousness and by comparing the impact of two different types of social norms on agent behavior and outcomes. The results show that the choice of social norms influences the accuracy of the agents' responses to varying environmental factors, as well as the effectiveness of social consciousness and other aspects of agents' utility functions. In experiments using heterogeneous groups of agents, both sets of norms were susceptible to the free-rider effect. However, the gains of the less responsible agents were minimal, suggesting that agent designers would have little incentive to design agents that deviate from the standard level of responsibility to the group.Engineering and Applied Science

DESIGN FOR FAST REQUEST FULFILLMENT OR NATURAL INTERACTION? INSIGHTS FROM AN EXPERIMENT WITH A CONVERSATIONAL AGENT

Conversational agents continue to permeate our lives in different forms, such as virtual assistants on mobile devices or chatbots on websites and social media. The interaction with users through natural language offers various aspects for researchers to study as well as application domains for practitioners to explore. In particular their design represents an interesting phenomenon to investigate as humans show social responses to these agents and successful design remains a challenge in practice. Compared to digital human-to-human communication, text-based conversational agents can provide complementary, preset answer options with which users can conveniently and quickly respond in the interaction. However, their use might also decrease the perceived humanness and social presence of the agent as the user does not respond naturally by thinking of and formulating a reply. In this study, we conducted an experiment with N=80 participants in a customer service context to explore the impact of such elements on agent anthropomorphism and user satisfaction. The results show that their use reduces perceived humanness and social presence yet does not significantly increase service satisfaction. On the contrary, our findings indicate that preset answer options might even be detrimental to service satisfaction as they diminish the natural feel of human-CA interaction

Inflation Targeting and the Role of Real Objectives

In this paper, I focus on two aspects of central banking – flexibility and transparency – that have been affected by monetary policy debates over the past twenty years. Because criticism of inflation targeting, at least in the United States, often focuses on the claim that an inflation targeting central bank may ignore real economic fluctuations, I direct my comments to the role real objectives play in the design of optimal monetary policy. That is, I focus on how flexible the central bank should be. I argue that, while the recent trend in the academic literature to view central bank objectives as derived from the welfare of the representative agent can be insightful, this perspective is not the only one for thinking about the goals assigned to the central bank. There are reasons why the objectives of a central bank should, potentially, deviate from social welfare, and I will focus on two such reasons; one related to imperfect monitoring and accountability, the other arising from asymmetric information.Central Banks, Monetary Policy, Inflation Targeting.

Methodological comparison of agent models

Hybrid agent architectures comprise the radical change of paradigms in AI over the past decades by reconciling the different styles of reactive, deliberative, even social systems. They have been successfully applied to a range of complex real-world domains. Due to their originally informal background, a verification of design goals in derived implementations, theoretical foundations, and a detailed comparison with other agent models have not yet been obvious. The present work proposes a formal methodology to bridge the gap between theoretical and practical aspects especially of hybrid designs, such as the layered INTERRAP. The employed, connected stages of specification, i.e., architecture, computational model, theory, proof calculus, and implementation, also provide a yet unique framework for comparing heterogeneous agent models including unified and logic-based ones. Based on recent work on INTERRAP, we demonstrate that this methodology allows to compare state-of-the-art designs from robotics, AI, computer science, and cognitive science with respect to a spectrum of inherent properties along the two dimensions of abstraction and declarativity. This supports our claim that INTERRAP is a coherent and advanced account of layered agency including goal-oriented abstraction planning in on-line interaction with reactive skills and social reasoning. We also derive particular research issues to guide the future development of INTERRAP

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data

Nash Social Welfare in Multiagent Resource Allocation

We study different aspects of the multiagent resource allocation problem when the objective is to find an allocation that maximizes Nash social welfare, the product of the utilities of the individual agents. The Nash solution is an important welfare criterion that combines efficiency and fairness considerations. We show that the problem of finding an optimal outcome is NP-hard for a number of different languages for representing agent preferences; we establish new results regarding convergence to Nash-optimal outcomes in a distributed negotiation framework; and we design and test algorithms similar to those applied in combinatorial auctions for computing such an outcome directly

EEG theta and Mu oscillations during perception of human and robot actions.

The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other

Integrated methodological frameworks for modelling agent-based advanced supply chain planning systems: a systematic literature review

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging fieldPeer Reviewe