Active adaptation in human-agent collaborative interaction

When a human user interacts with an adaptive agent to achieve human-agent collaboration, active adaptation is considered to be one of the critical characteristics of the agent. In order to investigate the principal features of active adaptation, we developed a human-agent collaborative experimental environment called WAITER (waiter agent interactive training experimental restaurant) and conducted two types of experiments, a Wizard of OZ (WOZ) agent experiment and an autonomous agent experiment. The objective of these experiments is to observe how human users perceive the agents and change their instructions when interacting with adaptive agents. The results indicate that humans can recognize changes in the agent’s actions and change their instruction methods accordingly. This implies that active adaptation of the agents may encourage the adaptation of the human users and help to build an adaptation loop between them. The experimental results also suggest that active adaptation may play an important role in a human-agent collaborative task.     

Experiments Toward a Mutual Adaptive Speech Interface That Adopts the Cognitive Features Humans Use for Communication and Induces and Exploits Users' Adaptations

Interactive agents such as pet robots or adaptive speech interface systems that require forming a mutual adaptation process with users should have two competences. One of these is recognizing reward information from users' expressed paralanguage information, and the other is informing the learning system about the users by means of that reward information. The purpose of this study was to clarify the specific contents of reward information and the actual mechanism of a learning system by observing how 2 persons could create a smooth speech communication, such as that between owners and their pets.

A communication experiment was conducted to observe how human participants create smooth communication through acquiring meaning from utterances in languages they did not understand. Then, based on experimental results, a meaning-acquisition model that considers the following 2 assumptions was constructed: (a) To achieve a mutual adaptive relationship with users, the model needs to induce users' adaptation and to exploit this induced adaptation to recognize the meanings of a user's speech sounds; and (b) to recognize users' utterances through trial-and-error interaction regardless of the language used, the model should focus on prosodic information in speech sounds, rather than on the phoneme information on which most past interface studies have focused.

The results confirmed that the proposed model could recognize the meanings of users' verbal commands by using participants' adaptations to the model for its meaning-acquisition process. However, this phenomenon was observed only when an experimenter gave the participants appropriate instructions equivalent to catchphrases that helped users learn how to use and interact intuitively with the model. Thus, this suggested the need for a subsequent study to discover how to induce the participants' adaptations or natural behaviors without giving these kinds of instructions.     

Organisational adaptation of multi-agent systems in a peer-to-peer scenario

Organisations in multi-agent systems (MAS) have proven to be successful in regulating agent societies. Nevertheless, changes in agents’ behaviour or in the dynamics of the environment may lead to a poor fulfilment of the system’s purposes, and so the entire organisation needs to be adapted. In this paper we focus on endowing the organisation with adaptation capabilities, instead of expecting agents to be capable of adapting the organisation by themselves. We regard this organisational adaptation as an assisting service provided by what we call the Assistance Layer. Our generic Two Level Assisted MAS Architecture (2-LAMA) incorporates such a layer. We empirically evaluate this approach by means of an agent-based simulator we have developed for the P2P sharing network domain. This simulator implements 2-LAMA architecture and supports the comparison between different adaptation methods, as well as, with the standard BitTorrent protocol. In particular, we present two alternatives to perform norm adaptation and one method to adapt agents’ relationships. The results show improved performance and demonstrate that the cost of introducing an additional layer in charge of the system’s adaptation is lower than its benefits.     

WOZ experiments for understanding mutual adaptation

A robot that is easy to teach not only has to be able to adapt to humans but also has to be easily adaptable to. In order to develop a robot with mutual adaptation ability, we believe that it will be beneficial to first observe the mutual adaptation behaviors that occur in human–human communication. In this paper, we propose a human–human WOZ (Wizard-of-Oz) experiment setting that can help us to observe and understand how the mutual adaptation procedure occurs between human beings in nonverbal communication. By analyzing the experimental results, we obtained three important findings: alignment-based action, symbol-emergent learning, and environmental learning.     

Analysis of emergent symmetry breaking in collective decision making

We investigate a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent–agent encounters and is inspired by the aggregation behavior of honeybees. In this article, we define symmetry breaking, several symmetry breaking measures, and report the phenomenon of emergent symmetry breaking within our observed system. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed and two macroscopic features are determined that significantly influence the symmetry breaking behavior. In addition, we investigate the connection between the ability of the MAS to break symmetries and the ability to stay flexible in a dynamic environment.     

Facility layout design using virtual multi-agent system

This paper presents a novel approach to the facility layout design problem based on multi-agent society where agents’ interactions form the facility layout design. Each agent corresponds to a facility with inherent characteristics, emotions, and a certain amount of money, forming its utility function. An agent’s money is adjusted during the learning period by a manager agent while each agent tries to tune the parameters of its utility function in such a way that its total layout cost can be minimized in competition with others. The agents’ interactions are formed based on market mechanism. In each step, an unoccupied location is presented to all applicant agents, for which each agent proposes a price proportionate to its utility function. The agent proposing a higher price is selected as the winner and assigned to that location by an appropriate space-filling curve. The proposed method utilizes the fuzzy theory to establish each agent’s utility function. In addition, it provides a simulation environment using an evolutionary algorithm to form different interactions among the agents and makes it possible for them to experience various strategies. The experimental results show that the proposed approach achieves a lower total layout cost compared with state of the art methods.     

A virtual human agent model with behaviour based on feeling exhaustion

A computational agent model for monitoring and control of a virtual human agent’s resources and exhaustion is presented. It models a physically grounded intelligent decision making process within the agent model for physical effort to be spent. Simulation results are discussed, and a formal analysis is presented on conditions under which the agent model functions properly, for example, such that it can be used to avoid running out of resources. The model is related to a model for monitoring or simulating a person’s heart rate. Finally some validation experiments are briefly discussed.     

Mutual learning of multi-consciousness agents,including an ego for an autonomous vehicle

In this paper, we propose a multi-agent learning system for the control of an intelligent robot, based on a model of the human consciousnesses, including the ego. We pay attention to the intelligent learning processes of human beings. We try to give a robot a high learning ability by modeling the roles of the human consciousnesses, including the ego. In most ordinary methods, the instructions for learning are given from outside the system only. In the proposed method, the instructions are given not only from outside, but also from inside (from other agents in the system). Therefore, the robot can learn efficiently because it has more instructions than usual. The learning is also more flexible, since an agent learns by instructions from other agents while the learning agent and one of the instructing agents exchange roles according to changes in the environment. We experimentally verified that the proposed method is efficient by using an actual robot.     

From logic programming towards multi‐agent systems

In this paper we present an extension of logic programming (LP) that is suitable not only for the “rational” component of a single agent but also for the “reactive” component and that can encompass multi‐agent systems. We modify an earlier abductive proof procedure and embed it within an agent cycle. The proof procedure incorporates abduction, definitions and integrity constraints within a dynamic environment, where changes can be observed as inputs. The definitions allow rational planning behaviour and the integrity constraints allow reactive, condition‐action type behaviour. The agent cycle provides a resource‐bounded mechanism that allows the agent’s thinking to be interrupted for the agent to record and assimilate observations as input and execute actions as output, before resuming further thinking. We argue that these extensions of LP, accommodating multi‐theories embedded in a shared environment, provide the necessary multi‐agent functionality. We argue also that our work extends Shoham’s Agent0 and the BDI architecture. This revised version was published online in June 2006 with corrections to the Cover Date.     

Ergonomic evaluation of the angle of abduction in a computer numerically controlled electro discharge machine environment

Dynamic situations are not fully controlled and affected by uncertain human factors. Anthropometric considerations are important in the design of systems. Present research work have considered the ‘angle of abduction’, whose effect on operator’s performance has been ergonomically evaluated in a CNC-EDM environment. In this work, the experimental data are analyzed through an ANOVA using SPSS statistical software. The result indicates that the angle of abduction significantly affects the operator’s performance in a CNC-EDM interaction environment. Further analysis revealed that a 45 degree abduction angle gives the optimal performance as far as a human–machine interaction environment is concerned.     

Exploring agent support at the user interface in e-commerce applications

In this paper, we present some of the results from our ongoing research work in the area of ‘agent support’ for electronic commerce, particularly at the user interface level. Our goal is to provide intelligent agents to assist both the consumers and the vendors in an electronic shopping environment. Users with a wide variety of different needs are expected to use the electronic shopping application and their expectations about the interface could vary a lot. Traditional studies of user interface technology have shown the existence of a ‘gap’ between what the user interface actually lets the users do and the users’ expectations. Agent technology, in the form of personalized user interface agents, can help to narrow this gap. Such agents can be used to give a personalized service to the user by knowing the user’s preferences. By doing so, they can assist in the various stages of the users’ shopping process, provide tailored product recommendations by filtering information on behalf of their users and reduce the information overload. From a vendor’s perspective, a software sales agent could be used for price negotiation with the consumer. Such agents would give the flexibility offered by negotiation without the burden of having to provide human presence to an online store to handle such negotiations. Published online: 25 July 2001     

Biological movement increases acceptance of humanoid robots as human partners in motor interaction

The automatic tendency to anthropomorphize our interaction partners and make use of experience acquired in earlier interaction scenarios leads to the suggestion that social interaction with humanoid robots is more pleasant and intuitive than that with industrial robots. An objective method applied to evaluate the quality of human–robot interaction is based on the phenomenon of motor interference (MI). It claims that a face-to-face observation of a different (incongruent) movement of another individual leads to a higher variance in one’s own movement trajectory. In social interaction, MI is a consequence of the tendency to imitate the movement of other individuals and goes along with mutual rapport, sense of togetherness, and sympathy. Although MI occurs while observing a human agent, it disappears in case of an industrial robot moving with piecewise constant velocity. Using a robot with human-like appearance, a recent study revealed that its movements led to MI, only if they were based on human prerecording (biological velocity), but not on constant (artificial) velocity profile. However, it remained unclear, which aspects of the human prerecorded movement triggered MI: biological velocity profile or variability in movement trajectory. To investigate this issue, we applied a quasi-biological minimum-jerk velocity profile (excluding variability in the movement trajectory as an influencing factor of MI) to motion of a humanoid robot, which was observed by subjects performing congruent or incongruent arm movements. The increase in variability in subjects’ movements occurred both for the observation of a human agent and for the robot performing incongruent movements, suggesting that an artificial human-like movement velocity profile is sufficient to facilitate the perception of humanoid robots as interaction partners.     

Reinforcement learning technique using agent state occurrence frequency with analysis of knowledge sharing on the agent’s learning process in multiagent environments

Reinforcement learning techniques like the Q-Learning one as well as the Multiple-Lookahead-Levels one that we introduced in our prior work require the agent to complete an initial exploratory path followed by as many hypothetical and physical paths as necessary to find the optimal path to the goal. This paper introduces a reinforcement learning technique that uses a distance measure to the goal as a primary gauge for an autonomous agent’s action selection. In this paper, we take advantage of the first random walk to acquire initial information about the goal. Once the agent’s goal is reached, the agent’s first perceived internal model of the environment is updated to reflect and include said goal. This is done by the agent tracing back its steps to its origin starting point. We show in this paper, no exploratory or hypothetical paths are required after the goal is initially reached or detected, and the agent requires a maximum of two physical paths to find the optimal path to the goal. The agent’s state occurrence frequency is introduced as well and used to support the proposed Distance-Only technique. A computation speed performance analysis is carried out, and the Distance-and-Frequency technique is shown to require less computation time than the Q-Learning one. Furthermore, we present and demonstrate how multiple agents using the Distance-and-Frequency technique can share knowledge of the environment and study the effect of that knowledge sharing on the agents’ learning process.     

Mathematics for/from Society: The Role of the Module in Modernizing Japanese Architectural Production

This paper presents an examination of the process of the development of module in the works and theories of Japanese architect Ikebe Kiyoshi (1920-79). Ikebe based his idea of module on the belief that “Beauty is Mathematics.” He applied his ideas of module in various ways from the 1940s to the 1970s. Analyzing his ideas and works against their historical background, the social and creative meanings of the idea of module and of mathematics in architecture will be re-examined. This allows us to see how Ikebe developed his ideas of module from a characteristic mathematical approach, and how he developed his idea of mathematical logic into his creative theories based on the flexible nature of people’s lifestyles and social conditions. Going beyond the cultural and social differences and the limitations of Le Corbusier’s Modulor, the idea of module as the method for organizing human space in a harmonious manner was reframed in Ikebe’s works, and was developed in a more flexible mathematical way.     

How do Experts Adapt their Explanations to a Layperson’s Knowledge in Asynchronous Communication? An Experimental Study

Despite a plethora of recommendations for personalization techniques, such approaches often lack empirical justification and their benefits to users remain obscure. The study described in this paper takes a step towards filling this gap by introducing an evidence-based approach for deriving adaptive interaction techniques. In a dialogue experiment with 36 dyads of computer experts and laypersons, we observed how experts tailored their written explanations to laypersons’ communicational needs. To support adaptation, the experts in the experimental condition were provided with information about the layperson’s knowledge level. In the control condition, the experts had no available information. During the composition of their answers, the experts in both conditions articulated their planning activities. Compared with the control condition, the experts in the experimental condition made a greater attempt to form a mental model about the layperson’s knowledge. As a result, they varied the type and proportion of the information they provided depending on the layperson’s individual knowledge level. Accordingly, such adaptive explanations helped laypersons reduce comprehension breakdowns and acquire new knowledge. These results provide evidence for theoretical assumptions regarding cognitive processes in text production and conversation. They empirically ground and advance techniques for adaptation of content in adaptive hypermedia systems. They are suggestive of ways in which explanations in recommender and decision support systems could be effectively adapted to the user’s knowledge background and goals.     

Learning to trust in the competence and commitment of agents

For agents to collaborate in open multi-agent systems, each agent must trust in the other agents’ ability to complete tasks and willingness to cooperate. Agents need to decide between cooperative and opportunistic behavior based on their assessment of another agents’ trustworthiness. In particular, an agent can have two beliefs about a potential partner that tend to indicate trustworthiness: that the partner is competent and that the partner expects to engage in future interactions. This paper explores an approach that models competence as an agent’s probability of successfully performing an action, and models belief in future interactions as a discount factor. We evaluate the underlying decision framework’s performance given accurate knowledge of the model’s parameters in an evolutionary game setting. We then introduce a game-theoretic framework in which an agent can learn a model of another agent online, using the Harsanyi transformation. The learning agents evaluate a set of competing hypotheses about another agent during the simulated play of an indefinitely repeated game. The Harsanyi strategy is shown to demonstrate robust and successful online play against a variety of static, classic, and learning strategies in a variable-payoff Iterated Prisoner’s Dilemma setting.     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

The elements of probabilistic time geography

Time geography uses space–time volumes to represent the possible locations of a mobile agent over time in a x–y–t space. A volume is a qualitative representation of the fact that the agent is at a particular time t _i inside of the volume’s base at t _i . Space–time volumes enable qualitative analysis such as potential encounters between agents. In this paper the qualitative statements of time geography will be quantified. For this purpose an agent’s possible locations are modeled from a stochastic perspective. It is shown that probability is not equally distributed in a space–time volume, i.e., a quantitative analysis cannot be based simply on proportions of intersections. The actual probability distribution depends on the degree of a priori knowledge about the agent’s behavior. This paper starts with the standard assumption of time geography (no further knowledge), and develops the appropriate probability distribution by three equivalent approaches. With such a model any analysis of the location of an agent, or relations between the locations of two agents, can be improved in expressiveness as well as accuracy.     

User—robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy

This paper describes a hands-off socially assistive therapist robot designed to monitor, assist, encourage, and socially interact with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot’s personality in the hands-off therapy process, focusing on the relationship between the level of extroversion–introversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward customized post-stroke rehabilitation therapy based on the user’s personality traits and task performance. Three validation experiment sets are described. The first maps the user’s extroversion–introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing. The second and the third experiments adjust the personality matching dynamically to adapt the robot’s therapy styles based on user personality and performance. The reported results provide first evidence for user preference for personality matching in the assistive domain and demonstrate how the socially assistive robot’s autonomous behavior adaptation to the user’s personality can result in improved human task performance. This work was supported by USC Women in Science and Engineering (WiSE) Program and the Okawa Foundation.