The purpose of this study is to determine the psychological meaning of coauthorship for a group of scientists, based on the assumption that the meaning of a concept is related to experience on “how a person behaves in a situation, depending on what the situation signifies to him”. The semantic meaning provides for an interpretation of action in beliefs, goals and intentions, following the idea that semantic meaning is a basis for inferring intentions to perform action. We used the Natural (...) Semantic Networks Method. We interviewed 162 scientists in the physical and natural sciences. Results show a cluster of the semantic meaning of coauthorship in which collaboration, teamwork, work, common interest, discussion and others, are retrieved from memory and represented as a natural semantic network of the concept of coauthorship. Collaboration and teamwork are the two concepts that have the highest semantic value and this holds true for various disciplines although teamwork is the most elicited word associated to coauthorship. We propose a model in which coauthorship represents the result of the experience, attitudes and behavior of a group process concerning collaboration and teamwork in science. (shrink)

What are the conditions under which groups of agents will perform well across multiple tasks? The author establishes a set of “alignment conditions” that enforce identity of beliefs and desires across agents. These conditions are necessary and sufficient for ensuring that a multiagent system behaves as if controlled by a rational centralized controller. Several widely observed social phenomena can be understood as facilitating the alignment conditions.

Collective intentionality is one of the most fundamental notions in social ontology. However, it is often thought to refer to a capacity which does not presuppose the existence of any other social facts. This chapter critically examines this view from the perspective of one specific theory of collective intentionality, the theory of Margaret Gilbert. On the basis of Gilbert’s arguments, the chapter claims that collective intentionality is a highly contingent achievement of complex social practices and, thus, not a basic social (...) phenomenon. The argument proceeds in three steps. First, Gilbert’s thesis that certain kinds of collective intentionality presuppose joint normative commitments is introduced. Second, it is argued that, on this view, individual commitments can only constitute the relevant kinds of collective intentional states if there are socially shared “principles of membership” that connect the force of individual commitments to a shared content. Third, it is shown that strong collective intentionality depends on the practical acceptance of shared norms and on the establishment of authority relations through mutual recognition. (shrink)

The importance of potential intentions has been demonstrated both in the construction of agent systems and in the formalisation of teamwork behaviour. However, there still lacks an adequate semantics for the notion of potential intentions as introduced by Grosz and Kraus in their SharedPlans framework. In this paper, we give a formal semantics to intentions and potential intentions, drawing upon both the representationalist approach and the accessibility-based approach. The model captures the dynamic relationship among intentions and potential intentions by providing (...) semantic rules and conditions. Also, the model treats norms as a special class of intentional context, and it is shown that norms play a critical role in the dynamics of intentions in multiagent systems. The formal semantics allows us to validate some properties that are widely-accepted for intention attitudes. (shrink)

We prove that for any finite deduction structure there exists a unique concise-widest chain-preserved split. Based on this result, we propose a logical splitting strategy which enables an agent to split its belief structure such that all the original inference chains can be preserved. The significance of such logical splitting at least is four-fold: It can be used by an agent to separate its concerns appropriately, or even create smaller and smarter clones which could save time and efforts in their (...) deliberation computing; It will enable an agent to adjust its concerns dynamically based on its recognition of the current situation, which may further enable the agent to make rapid and rational decisions when situation shifting occurs; In periodic team synchronization domains where agents can periodically synchronize their behaviors with no restriction on communication, it can be employed as a critical process for maintaining the shared mental models among a team of agents; and It is helpful in alleviating the complexity of reasoning about relevant information, and in better anticipating both explicit and implicit information needs based on the recognition of the current situation. (shrink)

Thesis: Macro-level social phenomena are implemented through the (social) actions and minds of the individuals. Without an explicit theory of the agents' minds that founds, agents' behavior we cannot understand macro-level social phenomena, and in particular how they work. AntiThesis: Mind is not enough: the theory of individual (social) mind and action is not enough to explain several macro-level social phenomena. First, there are pre-cognitive, objective social structures that constrain the actions of the agents; second, there are emergent, unaware or (...) non-contractual forms of cooperation, organisation, and intelligence. Synthesis: The real challenge is how to reconcile, cognition with emergence, intention and deliberation with unknown or unplanned social functions and social order . Both objective structures and unplanned self-organising complex forms of social order and social function emerge from the interactions of agents and from their individual mental states; both these structures and self-organising systems feedback on agents' behaviors through the agents' individual minds. (shrink)

This paper explores the assumption of autonomy. Several arguments are presented against the assumption of runtime autonomy as a principle of design for artificial intelligence systems. The arguments vary from being theoretical, to practical, and to analytic. The latter parts of the paper focus on one strategy for building non-autonomous systems (the practice view). One critical theme is that intelligence is not located in the system alone, it emerges from a history of interactions among user, builder, and designer over a (...) given set of data as mediated by the system. A second critical theme is that artificially intelligent systems are ongoing projects that must be continuously adapted and revised using joint person-machine efforts. (shrink)

Conventional behaviors develop from practice for regularly occurring problems of coordination within a community of actors. Reusing and extending conventional methods for coordinating behavior is the task of everyday reasoning.The computational model presented in the paper details the emergence of convention in circumstances where there is no ruling body of knowledge developed by prior generations of actors within the community to guide behavior. The framework we assume combines social theories of cognition with human information processing models that have been developed (...) within Cognitive Science. The model presented reflects both elements of the framework. Conventional behaviors are partially coded in the predisposition of participants in a joint activity to expect certain points of coordination to develop during the course of the activity. The expected points of coordination that are commonly assumed form a design for an activity. Because of uncertainty, interruptions, and numerous other opportunities to get off‐track and out‐of‐synch, the participants must work jointly and continuously to achieve conventional coordination.One feature of the model is that the community improves its performance despite the fact that individual actors reason independently about their experiences. Another important feature of the model is that the mechanisms for improving behavior are tied to the memory function of individual actors. A third important feature is that the social interaction among the participants simplifies and drives the everyday reasoning processes. An analysis of a large set of computational experiments supports the theoretical position that is developed regarding everyday reasoning and convention. (shrink)

This paper develops Lewis’ notion of convention within a framework that mixes cognitive science with some more social theories of activity like distributed cognition and activity theory. The close examination of everyday situations of convention-based activity will produce some interesting issues for a cognitive theory of behavior. Uncertainty, dynamics, and the complexities of the performance of convention-based activities that are distributed over time and/or place, are driving factors in the analysis that is presented. How the actors reason and manage their (...) collaboration is characterized as pragmatic action. During the course of recurrent activities, the participants adapt previously learned convention-based activities to new circumstances. The coordinating representations that are a part of the design of the context mediate parts of the activity. As they act, the participants learn. (shrink)

I show how a conversational process that takes simple, intuitively meaningful steps may be understood as a sophisticated computation that derives the richly detailed, complex representations implicit in our knowledge of language. To develop the account, I argue that natural language is structured in a way that lets us formalize grammatical knowledge precisely in terms of rich primitives of interpretation. Primitives of interpretation can be correctly viewed intentionally, as explanations of our choices of linguistic actions; the model therefore fits our (...) intuitions about meaning in conversation. Nevertheless, interpretations for complex utterances can be built from these primitives by simple operations of grammatical derivation. In bridging analyses of meaning at semantic and symbol‐processing levels, this account underscores the fundamental place for computation in the cognitive science of language use. (shrink)

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. (...) We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration. (shrink)

This chapter investigates the computational consequences of a broadly Gricean view of language use as intentional activity. In this view, dialogue rests on coordinated reasoning about communicative intentions. The speaker produces each utterance by formulating a suitable communicative intention. The hearer understands it by recognizing the communicative intention behind it. When this coordination is successful, interlocutors succeed in considering the same intentions— that is, the same representations of utterance meaning—as the dialogue proceeds. In this paper, I emphasize that these intentions (...) can be formalized; we can provide abstract but systematic representations that spell out what a speaker is trying to do with an utterance. Such representations describe utterances simultaneously as the product of our knowledge of grammar and as actions chosen for a reason. In particular, they must characterize the speaker’s utterance in grammatical terms, provide the links to the context that the grammar requires, and so arrive at a contribution that the speaker aims to achieve. Because I have implemented this formalism, we can regard it as a possible analysis of conversational processes at the level of computational theory. Nevertheless, this analysis leaves open what the nature of the biological computation involved in inference to intentions is, and what regularities in language use support this computation. (shrink)

Team members co-regulate their activities and move together at the collective level of behavior while coordinating their actions toward shared goals. In parallel with team processes, team members need to resolve uncertainties arising from the changing task and environment. In this exploratory study we have measured the differential neurodynamics of seven two-person healthcare teams across time and brain regions during autonomous and collaborative segments of simulation training. The questions posed were: whether these abstract and mostly integrated constructs could be separated (...) neurodynamically; and, what could be learned about taskwork and teamwork by trying to do so? The taskwork and teamwork frameworks used were Neurodynamic Information, an electroencephalography derived measure shown to be a neurodynamic proxy for the pauses and hesitations associated with individual uncertainty, and inter-brain EEG coherence which is a required component of social interactions. No interdependency was observed between NI and IBC, and second-by-second dynamic comparisons suggested mutual exclusivity. These studies show that proxies for fundamental properties of teamwork and taskwork can be separated neurodynamically during team performances of ecologically valid tasks. The persistent expression of NI and IBC were not simultaneous suggesting that it may be difficult for team members to maintain inter-brain coherence while simultaneously reducing their individual uncertainties. Lastly, these separate dynamics occur over time frames of 15–30 s providing time for real-time detection and mitigation of individual and collaborative complications during training or live patient encounters. (shrink)

This article discusses the English construction variously known as Locative Inversion or Stylistic Inversion. It shows that the construction displays a unique ensemble of grammatical and usage properties that can be stated but not motivated through purely grammatical means. In search of an explanatory approach, an analysis is presented that draws on concepts of Artificial Intelligence, in particular plans viewed as complex mental attitudes. It is claimed that utterances of Locative Inversion are associated with a particular communicative plan on the (...) speaker's part that motivates the grammatical and usage properties of the construction where grammar alone cannot. The final section of the article presents corpus examples showing that Locative Inversion is indeed used in the contexts predicted by the plan associated with the construction. This result is both theoretically and methodologically significant, insofar as it constitutes a case where the grammatical properties of a syntactic construction are better explained through its typical conditions of use than through grammatical means proper. (shrink)