Wan Ching Ho

Computational memory architectures for autobiographic agents interacting in a complex virtual environment: a working model

In this paper, we discuss the concept of autobiographic agent and how memory may extend an agents temporal horizon and increase its adaptability. These concepts are applied to an implementation of a scenario where agents are interacting in a complex virtual artificial life environment. We present computational memory architectures for autobiographic virtual agents that enable agents to retrieve meaningful information from their dynamic memories which increases their adaptation and survival in the environment. The design of the memory architectures, the agents, and the virtual environment are described in detail. Next, a series of experimental studies and their results are presented which show the adaptive advantage of autobiographic memory, i.e. from remembering significant experiences. Also, in a multi-agent scenario where agents can communicate via stories based on their autobiographic memory, it is found that new adaptive behaviours can emerge from an individuals reinterpretation of experiences received from other agents whereby higher communication frequency yields better group performance. An interface is described that visualises the memory contents of an agent. From an observer perspective, the agents’ behaviours can be understood as individually structured, and temporally grounded, and, with the communication of experience, can be seen to rely on emergent mixed narrative reconstructions combining the experiences of several agents. This research leads to insights into how bottom-up story-telling and autobiographic reconstruction in autonomous, adaptive agents allow temporally grounded behaviour to emerge. The article concludes with a discussion of possible implications of this research direction for future autobiographic, narrative agents.

A Socially-Aware Memory for Companion Agents

Memory is a vital capability for intelligent social Companions. In this paper, we introduce a simple memory model that allows a Companion to maintain a long-term relationship with the user by remembering past experiences in order to personalise interaction. Additionally, we implemented a situational forgetting mechanism that gives the Companion the ability to protect the users privacy by not disclosing sensitive data. Two test scenarios are used to demonstrate these abilities in our Companions.

An initial memory model for virtual and robot companions supporting migration and long-term interaction

This work proposes an initial memory model for a long-term artificial companion, which migrates among virtual and robot platforms based on the context of interactions with the human user. This memory model enables the companion to remember events that are relevant or significant to itself or to the user. For other events which are either ethically sensitive or with a lower long-term value, the memory model supports forgetting through the processes of generalisation and memory restructuring. The proposed memory model draws inspiration from the human short-term and long-term memories. The short-term memory will support companions in focusing on the stimuli that are relevant to their current active goals within the environment. The long-term memory will contain episodic events that are chronologically sequenced and derived from the companions interaction history both with the environment and the user. There are two key questions that we try to address in this work: 1) What information should the companion remember in order to generate appropriate behaviours and thus smooth the interaction with the user? And, 2) What are the relevant aspects to take into consideration during the design of memory for a companion that can have different types of virtual and physical bodies? Finally, we show an implementation plan of the memory model, focusing on issues of information grounding, activation and sensing based on specific hardware platforms.

Towards a Narrative Mind: The Creation of Coherent Life Stories for Believable Virtual Agents

This paper describes an approach to create coherent life stories for Intelligent Virtual Agents (IVAs) in order to achieve long-term believability. We integrate a computational autobiographic memory, which allows agents to remember significant past experiences and reconstruct their life stories from these experiences, into an emotion-driven planning architecture. Starting from the literature review on episodic memory modelling and narrative agents, we discuss design considerations for believable agents which interact with users repeatedly and over a long period of time. In the main body of the paper we present the narrative structure of human life stories. Based on this, we incorporate three essential discourse units and other characteristics into the design of the autobiographic memory structure. We outline part of the implementation of this memory architecture and describe the plan for evaluating the architecture in long-term user studies.

Agents that remember can tell stories: integrating autobiographic memory into emotional agents

For the past few years many new applications are being developed featuring interactive environments populated with autonomous virtual agents capable of acting according to their goals, beliefs and even social relations. Such agents must be able to interact with each other, and more importantly with the user, thus involving the users in an engaging narrative experience. To achieve these, in this paper we describe the essential event structure in an autobiographic memory, event reconstructions in memory retrieval process and the influences of such past events in interpersonal relations.

Autobiographic knowledge for believable virtual characters

It has been widely acknowledged in the areas of human memory and cognition that behaviour and emotion are essentially grounded by autobiographic knowledge. In this paper we propose an overall framework of human autobiographic memory for modelling believable virtual characters in narrative story-telling systems and role-playing computer games. We first lay out the background research of autobiographic memory in Psychology, Cognitive Science and Artificial Intelligence. Our autobiographic agent framework is then detailed with features supporting other cognitive processes which have been extensively modelled in the design of believable virtual characters (e.g. goal structure, emotion, attention, memory schema and reactive behaviour-based control at a lower level). Finally we list directions for future research at the end of the paper.

Comparing Different Control Architectures for Autobiographic Agents in Static Virtual Environments

This paper highlights the effectiveness of autobiographic memory applied to an autonomous agent from an Artificial Life perspective. A virtual experimental-based approach deals with different implementation designs of control architectures for autobiographic agents, including detailed measurements of the agents’ lifetimes compared with purely reactive agents, running in two distinct static environments. Experimental results produce evidence which confirms the research hypothesis that autobiographic memory can prove beneficial, indicating increases in the lifetime of an autonomous, autobiographic, minimal agent. In particular, both Trace-back and Locality autobiographic memory architectures, with or without noise interference, showed superiority over purely reactive control.

Exploring the use of robots as social mediators in a remote human-human collaborative communication experiment

Our long-term goal is to develop robots as social mediators that can support human-human communication in remote interaction scenarios. This paper explores the effects of an autonomous robot on human-human remote communication and studies participants’ preferences in comparison with a communication system not involving robots. We developed a platform for remote human-human communication in the context of a collaborative computer game. The exploratory study involved twenty pairs of participants who communicated using video conference software. Participants expressed more social cues when using the robot and sharing of their game experiences with each other. However, analyses of the interactions of the participants with each other and with the robot show that it is difficult for participants to familiarise themselves quickly with the robot while they can perform the same task more efficiently with conventional devices. These issues need to be carefully considered and addressed when designing human-human remote communication systems with robots as social mediators.

Advocating an ethical memory model for artificial companions from a human-centred perspective

This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.

Integrating Constrained Experiments in Long-Term Human–Robot Interaction Using Task-and Scenario-Based Prototyping

In order to investigate how the use of robots may impact everyday tasks, twelve participants in our study interacted with a University of Hertfordshire Sunflower robot over a period of 8 weeks in the universitys Robot House. Participants performed two constrained tasks, one physical and one cognitive, four times over this period. Participant responses were recorded using a variety of measures including the System Usability Scale and the NASA Task Load Index. The use of the robot had an impact on the experienced workload of the participants differently for the two tasks, and this effect changed over time. In the physical task, there was evidence of adaptation to the robots behavior. For the cognitive task, the use of the robot was experienced as more frustrating in the later weeks.