A conceptual framework for interactive virtual storytelling

This paper presents a framework of an interactive storytelling system. It can integrate five components: management centre, evaluation centre, intelligent virtual agent, intelligent virtual environment, and users, making possible interactive solutions where the communication among these components is conducted in a rational and intelligent way. Environment plays an important role in providing heuristic information for agents through communicating with the management centre. The main idea is based on the principle of heuristic guiding of the behaviour of intelligent agents for guaranteeing the unexpectedness and consistent themes

Improvising Linguistic Style: Social and Affective Bases for Agent Personality

This paper introduces Linguistic Style Improvisation, a theory and set of algorithms for improvisation of spoken utterances by artificial agents, with applications to interactive story and dialogue systems. We argue that linguistic style is a key aspect of character, and show how speech act representations common in AI can provide abstract representations from which computer characters can improvise. We show that the mechanisms proposed introduce the possibility of socially oriented agents, meet the requirements that lifelike characters be believable, and satisfy particular criteria for improvisation proposed by Hayes-Roth.Comment: 10 pages, uses aaai.sty, lingmacros.sty, psfig.st

A Subsumption Agent for Collaborative Free Improvisation

This paper discusses the design and evaluation of an artificial agent for collaborative musical free improvisation. The agent provides a means to investigate the underpinnings of improvisational interaction. In connection with this general goal, the system is also used here to explore the implementation of a collaborative musical agent using a specific robotics architecture, Subsumption. The architecture of the system is explained, and its evaluation in an empirical study with expert improvisors is discussed. A follow-up study using a second iteration of the system is also presented. The system design and connected studies bring together Subsumption robotics, ecological psychology, and musical improvisation, and contribute to an empirical grounding of an ecological theory of improvisation

Collaborative Creativity:Information-Driven Coordination Dynamics and Prediction in Movement and Musical Improvisation

Humans collaborate with a large number of people in order to create and accomplish incredible feats. We argue that rich coordination dynamics underpin our capacity for collaborative creativity. These dynamics characterize the ways in which people are able to covary their thoughts, actions, behavior, etc. for functional purposes. We investigated the coordination dynamics of improvisation as a special case of collaborative creativity using two openly available data sets: a movement-based mirror game and jazz piano improvisation. By focusing on improvisation, the tasks elicit the need for real-time adaptation and mutual prediction based on information exchange between interacting individuals, with the creative ‘product’ being the behavioral performance itself. For each data set, we performed a transfer entropy analysis as well as an estimate of prediction decay. The combination of these two methods allows us to understand the dynamics as information-driven coordination flow and to differentiate unidirectional influence from mutual influence as well as the predictability of signals exhibited during collaborative creativity. We observed that for the mirror game, experts and novices exhibited unidirectional and bidirectional influence on each other’s movements largely independent of their improvisational experience level. Further, movement improvisation signals generated by experts were generally more predictable than those of novices. In terms of the jazz improvisation, our results showed evidence of bidirectional influence between the onset densities of coupled and one-way improvisational dyads, and the predictability of the signal did not vary systematically across these conditions. We discuss these findings in terms of differences between improvisational contexts, methodical challenges, and future directions

Collaborative Creativity:Information-Driven Coordination Dynamics and Prediction in Movement and Musical Improvisation

Humans collaborate with a large number of people in order to create and accomplish incredible feats. We argue that rich coordination dynamics underpin our capacity for collaborative creativity. These dynamics characterize the ways in which people are able to covary their thoughts, actions, behavior, etc. for functional purposes. We investigated the coordination dynamics of improvisation as a special case of collaborative creativity using two openly available data sets: a movement-based mirror game and jazz piano improvisation. By focusing on improvisation, the tasks elicit the need for real-time adaptation and mutual prediction based on information exchange between interacting individuals, with the creative ‘product’ being the behavioral performance itself. For each data set, we performed a transfer entropy analysis as well as an estimate of prediction decay. The combination of these two methods allows us to understand the dynamics as information-driven coordination flow and to differentiate unidirectional influence from mutual influence as well as the predictability of signals exhibited during collaborative creativity. We observed that for the mirror game, experts and novices exhibited unidirectional and bidirectional influence on each other’s movements largely independent of their improvisational experience level. Further, movement improvisation signals generated by experts were generally more predictable than those of novices. In terms of the jazz improvisation, our results showed evidence of bidirectional influence between the onset densities of coupled and one-way improvisational dyads, and the predictability of the signal did not vary systematically across these conditions. We discuss these findings in terms of differences between improvisational contexts, methodical challenges, and future directions

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Topologies of agents interactions in knowledge intensive multi-agentsystems for networked information services

Agents in a multi-agent system (mAS) could interact and cooperate in many different ways. The topology of agent interaction determines how the agents control and communicate with each other, what are the control and communication capabilities of each agent and the whole system, and how efficient the control and communications are. In consequence, the topology affects the agents’ ability to share knowledge, integrate knowledge, and make efficient use of knowledge in MAS. This paper presents an overview of four major MAS topologic models, assesses their advantages and disadvantages in terms of agent autonomy, adaptation, scalability, and efficiency of cooperation. Some insights into the applicability for each of the topologies to different environment and domain specific applications are explored. A design example of the topological models to an information service management application is attempted to illustrate the practical merits of each topology

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science