Multi-agent literature explores personifying artificial agents with personality, emotions or cognitive biases to produce “typical”, believable agents. In this study, we demonstrate the potential of endowing artificial agents with a motivation, using human implicit motivation psychology theory that introduces 3 motive profiles - power, achievement and affiliation, to create diverse, risk-aware agents. We first devise a framework to model these motivated agents (or agents with any inherent behavior), that can activate different strategies depending on the circumstances. We conduct experiments on a fire-fighting task domain, evaluate how motivated teams perform, and draw conclusions on appropriate team compositions to be deployed in environments with different risk levels. Our framework generates predictable agents as their resulting behaviors align with the inherent characteristics of their motives. We find that motivational diversity within teams is beneficial in dynamic collaborative environments, especially as the task risk level increases. Furthermore, we observed that the best composition in terms of the performance metrics used to evaluate team compositions, does not remain the same as the collaboration level required to achieve goals changes. These results have implications for future designs of risk-aware autonomous teams and Human-AI teams, as they highlight the prospects of creating better artificial teammates and performance gains that could be achieved through anthropomorphized motivated agents.

This paper presents a grammar-based evolutionary model to facilitate autonomous emergence of task allocation for intelligent multi-agent systems. The approach adopts a context-free grammar to determine the behaviour rule syntax. This allows for flexibility in evolving task allocation under multiple and dynamic constraints without manual rule design and parameter tuning. Experimental evaluations conducted with a target discovery simulation illustrate that the grammar-based model performs successfully in both dynamic and non-dynamic conditions. A statistically significant performance improvement is shown compared to an algorithm developed with the broadcast of local eligibility mechanism and a genetic programming mechanism. Grammatical evolution can achieve near-optimal solutions under restrictions applied on the number of agents, targets and the time allowed. Further, analysis of the evolved rule structures shows that grammatical evolution can identify less complex rule structures for behaviours while maintaining the expected level of performance. The results infer that the proposed model is a promising alternative for dynamic task allocation with human interactions in complex real-world domains.

This paper presents a grammar-based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi-agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar-based evolution can significantly improve the performance of an agent system in complex problem domains.

Trust has been widely recognized as particularly significant among the factors influencing team performance. Trust directly impacts team performance as it plays a pivotal role in the decisions that each team member (each agent) makes regarding their own actions and their interactions with fellow team members (other agents). Hence, determining the appropriate actions of each agent in the team based on perceived trust information is critical to ensure optimal team performance. However, there is no generic mechanism dedicated to such a problem. This paper addresses this problem by proposing a novel trust architecture which integrates differentiated trust with response strategies. Differentiated trust is multidimensional trust with each trust dimension representing trust in an agent's abilities to perform the task associated with that dimension. With differentiated trust, an agent can differentiate the trustworthiness of another agent in performing different subtasks (a secondary task or a portion of the primary task). To further fulfill the transition from perceptual trust to practical actions, responses strategies are introduced. Each response strategy associates trust levels with the available actions through a distinct deterministic strategy. The high dimensional trust enabled by the differentiated trust is used as the input of a response strategy for more nuanced manipulation of the interactions. The impact of the proposed trust architecture is demonstrated through an experimental investigation. A platform simulating team performance is built based on a food foraging task. Scenarios embodying different types and proportions of a priori flawed agents are introduced to enable the system to distinguish between agents in terms of trust; thus, providing a potential to optimize team performance through the trust architecture. It is demonstrated that the proposed trust architecture enables optimized team performance in various scenarios involving agents with different trustworthiness by the appropriate determination of each agent's actions in the interactive teamwork.

Considerable progress has been made in improving the estimation accuracy of cognitive workload using various sensor technologies. However, the overall performance of different algorithms and methods remain suboptimal in real-world applications. Some studies in the literature demonstrate that a single modality is sufficient to estimate cognitive workload. These studies are limited to controlled settings, a scenario that is significantly different from the real world where data gets corrupted, interrupted, and delayed. In such situations, the use of multiple modalities is needed. Multimodal fusion approaches have been successful in other domains, such as wireless-sensor networks, in addressing single-sensor weaknesses and improving information quality/accuracy. These approaches are inherently more reliable when a data source is lost. In the cognitive workload literature, sensors, such as electroencephalography (EEG), electrocardiography (ECG), and eye tracking, have shown success in estimating the aspects of cognitive workload. Multimodal approaches that combine data from several sensors together can be more robust for real-time measurement of cognitive workload. In this article, we review the published studies related to multimodal data fusion to estimate the cognitive workload and synthesize their main findings. We identify the opportunities for designing better multimodal fusion systems for cognitive workload modeling.

Active video games have proven to be an effective means of coupling gaming and exercise, while also providing a more enjoyable experience. In this article, we investigate two elliptical walker-based exertion systems for active video game locomotion. The impacts on heart rate, respiratory rate, and sense of presence were investigated as a result of using different locomotion systems by employing 20 subjects. Four configurations of elliptical walker-based exertion systems were compared with two commonly used sedentary game controllers. The results showed that participants' mean heart and respiratory rate were significantly increased as a result of using the active locomotion systems. Seventy percent of participants stated they would likely use the exertion systems at home, with a mean estimated weekly use time of 4.2 h. It is expected that the ongoing use of the exertion systems would exceed the physical activity guidelines recommended by the World Health Organisation. While participants subjectively did not feel more present, they were more involved with the virtual experience when using the exertion systems compared to the sedentary configurations. It was found that familiarity with the controls, and weekly time spent playing video games both had a significant impact on player exertion and subjective presence.

With the continuous technological advancement in the game industry, millions of players engage in various online games everyday. Player population size of games ebb and flow through time as a complex series. Analyzing these player population numbers in a shorter time window, such as weekly, could help generate insights that enrich the understanding about low-level population fluctuation patterns of online games. However, this area of game data analytics still has space for further enhancement. This study focuses on discovering patterns of weekly player population fluctuations, that could aid in comprehending how the population of various kind of games change within a framing window of a week. We use player population time series of 1963 games available on Steam. Utilizing several trend removal techniques and conducting seasonality detection we identify that 77% of games display a recurring weekly pattern in player population fluctuations. Moreover, our dynamic time warping based cluster analysis shows that there are 9 diverse weekly player population fluctuation patterns. Among these 9, the governing pattern visible in the majority of games displays that the player population is higher towards the weekend. Finally, we scrutinize the tags, age requirements and overall population size of games in each cluster associated with the diverse patterns to generate insights about the characteristics of games associated with each weekly population pattern.

This paper presents an evolutionary computing based approach to automatically synthesise swarm behavioural rules from their atomic components, thus making a step forward in trying to mitigate human bias from the rule generation process, and leverage the full potential of swarm systems in the real world by modelling more complex behaviours. We identify four components that make-up the structure of a rule: control structures, parameters, logical/relational connectives and preliminary actions, which form the rule space for the proposed approach. A boids simulation system is employed to evaluate the approach with grammatical evolution and genetic programming techniques using the rule space determined. While statistical analysis of the results demonstrates that both methods successfully evolve desired complex behaviours from their atomic components, the grammatical evolution model shows more potential in generating complex behaviours in a modularised approach. Furthermore, an analysis of the structure of the evolved rules implies that the genetic programming approach only derives non-reusable rules composed of a group of actions that is combined to result in emergent behaviour. In contrast, the grammatical evolution approach synthesises sound and stable behavioural rules which can be extracted and reused, hence making it applicable in complex application domains where manual design is infeasible.

With the fast evolution in the area of processing units and sensors, wearable devices are becoming more popular among people of all ages. Recently, there has been renewed interest in exploiting the capabilities of wearable sensors for person recognition while undertaking their normal daily activities. In this paper, we focus on utilizing motion information of known daily activities gathered from wearable sensors for recognizing the person. The analysis of the results demonstrates that different fundamental classification factors have an impact on person recognition success-rates. Furthermore, the results of comparison among subjects prove that some subjects have high classification results and can be easily identifiable compared to other subjects which have high confusability rates. Lastly, a significant improvement in subject classification success rate was found for activities with little or no movement which can successfully distinguish among persons and hence producing higher classification results compared to activities with large movement.

EEG signals could reveal unique information of an individual's brain activities. They have been regarded as one of the most promising biometric signals for person identification and verification. Steady-State Visual Evoked Potentials (SSVEPs), as EEG responses to visual stimulations at specific frequencies, could provide biometric information. However, current methods on SSVEP biometrics with hand-crafted power spectrum features and canonical correlation analysis (CCA) present only a limited range of individual distinctions and suffer relatively low accuracy. In this paper, we propose convolution neural networks (CNNs) with raw SSVEPs for person identification and verification without the need for any hand-crafted features. We conduct a comprehensive comparison between the performance of CNN with raw signals and a number of classical methods on two SSVEP datasets consisting of four and ten subjects, respectively. The proposed method achieved an averaged identification accuracy of 96.8%±0.01, which outperformed the other methods by an average of 45.5% (p-value <; 0.05). In addition, it achieved an averaged False Acceptance Rate (FAR) of 1.53%±0.01 and True Acceptance Rate (TAR) of 97.09%±0.02 for person verification. The averaged verification accuracy is 98.34% ± 0.01, which outperformed the other methods by an average of 11.8% (p-value <; 0.05). The proposed method based on deep learning offers opportunities to design a general-purpose EEG-based biometric system without the need for complex pre-processing and feature extraction techniques, making it feasible for real-time embedded systems.

There are many application domains, where hierarchical data is inherent, but surprisingly, there are few techniques for mining patterns from such important data. Hierarchical Heavy Hitters (HHH) and multilevel and Cross-Level Association Rules (CLAR) mining are well-known hierarchical pattern mining techniques. The problem in these techniques; however, is that they focus on capturing only global patterns from data but cannot identify local contextual patterns. Another problem in these techniques is that they treat all data items in the transaction equally and do not consider the sequential nature of the relationship among items within a transaction; hence, they cannot capture the correlation semantic within the transactions of the data items. There are many applications such as clickstream mining, healthcare data mining, network monitoring, and recommender systems, which require to identify local contextual patterns and correlation semantics. In this work, we introduce a new concept, which can capture the sequential nature of the relationship between pairs of hierarchical items at multiple concept levels and can capture local contextual patterns within the context of the global patterns. We call this notion Hierarchically Correlated Heavy Hitters (HCHH). Specifically, the proposed approach finds the correlation between items corresponding to hierarchically discounted frequency counts. We have provided formal definitions of the proposed concept and developed algorithmic approaches for computing HCHH in data streams efficiently. The proposed HCHH algorithm have deterministic error guarantees, and space bounds. It requires O(η/ϵpϵs) memory, where h is a small constant, and ϵp∈ [0,1], ϵs∈ [0,1] are user defined parameters on upper bounds of estimation error. We have compared the proposed HCHH concept with existing hierarchical pattern mining approaches both theoretically as well as experimentally.

Modern video games come with highly realistic graphics enabling the players to interact with visually rich virtual worlds. Realistic (life-like) animation of nonplayer characters (NPCs) in such virtual worlds is particularly important to enhance the gaming experience. Multiagent systems are one effective approach to synthesize life-like behaviors and interactions by codifying simple rules into NPCs (each NPC as an autonomous agent). However, such behaviors generally come at the cost of increasing computational expense and complexity in terms of aspects such as number of rules and parameters. Therefore, the desire for high fidelity (highly realistic) behaviors is often in conflict with the drive for low complexity. Multiobjective evolutionary algorithms provide a sophisticated mechanism to optimize two or more conflicting objectives simultaneously. However, evolutionary computing techniques need an appropriate objective function to drive the exploration in the correct direction. Pairing of evolutionary techniques and multiagent systems is challenging in the classes of problems in which the fitness is evaluated based on human aesthetic judgment rather than on objective forms of measurements. In this study, we present a multiobjective evolutionary framework to evolve low complexity and high fidelity multiagent systems by utilizing a machine learning system trained by bootstrapping human aesthetic judgment. We have gathered empirical data in three problem areas-simulation of conversational group dynamics, sheepdog herding behaviors, and traffic dynamics, and show the effectiveness of our approach in deriving low complexity and high fidelity multiagent systems. Further, we have identified common properties of the Pareto-optimal frontiers in the three problem areas that can ultimately lead to an understanding of a relationship between simulation model complexity and behavior fidelity. This understanding will be useful in deciding which level of behavioral fidelity is required for the characters in video games based on the distance to the camera, importance to the scene, and available computational resources.

Collaboration or teaming is a powerful strategy in which a group of agents work towards one or more common goals. Teaming imposes a base-load cognitive burden upon a participating agent - to be an effective team member the agent must maintain awareness of fellow team-members and employ that information in its decision-making processes. Via a multi-objective evolutionary approach (NSGA-II) the paper explores the Situational Awareness (SA) and computational processing costs necessary for an agent to participate effectively in a proto-team - a flock. Results show that relatively low levels of the available SA information or computational processing (frequency of application of the rules to determine desired velocity) are sufficient to ensure flocking is achieved and maintained. The proposed methodology is expandable to include human teams, human-machine hybrid teams, and exploration of centralised versus decentralised control.

This paper presents an EEG-based interactive genetic algorithm framework, with the goal of leveraging EEG signals collected from a human expert involved in the evaluation of interactive genetic algorithm as inputs for genetic parameter control. We explain the framework of the system and our cognitive model constructed based on a 19 channel EEG system. An experiment has been performed to test the effectiveness of our framework and our cognitive model. Our work is the first attempt to combine brain-computer interaction with interactive evolutionary computation and parameter control.

Sheepdog herding behaviors demonstrate an interesting form of interactions between two classes of agents - sheep and the dog. The nature of the interactions between sheep and the dog takes a special form of competition which is different to the traditional prey-predator interactions where the success of prey depends on the failure of the predator and vice versa. In consequent, the development of an appropriate objective function to efficiently co-evolve successful sheepdog herding behaviors becomes challenging. This paper presents a framework to efficiently co-evolve sheepdog herding behaviors utilizing the simple rule based agent approach in order to derive high fidelity behavior dynamics and discusses the challenges involved in the process.

Simple rule based Multi Agent Systems are widely used in the fields of social simulations and game artificial intelligence in order to incorporate the complexity and richness of action and interaction into the characters in the virtual environments while keeping computational cost low. This paper presents an approach to synthesize the spatio-temporal dynamics of groups in standing conversation: four simple spatial rules form the building-blocks and a framework to automatically evolve rule and the parameter space by bootstrapping a-priori human judgment on the aesthetic quality of the simulations is introduced. The framework consists of a Genetic Algorithm and a scorer (fitness function) developed based on a machine learning system trained using human evaluations. The results of the study suggest that the framework is capable of deriving optimal rule and parameter combinations utilizing only a relatively small set of human scored training data. Further, the relationship between rule-complexity and visual fidelity is explored.

In multiobjective optimization problems (MOPs), the Pareto set consists of efficient solutions that represent the best trade-offs between the conflicting objectives. Many forms of uncertainty affect the MOP, including uncertainty in the decision variables, parameters or objectives. A source of uncertainty that is not studied in the evolutionary multiobjective optimization (EMO) literature is the decision-maker's attitude to risk (DMAR) even though it has great significance in real-world applications. Often the decision-makers change over the course of the decision-making process and thus, some relevant information about preferences of future decision-makers is unknown at the time a decision is made. This poses a major risk to organizations because a new decision-maker may simply reject a decision that has been made previously. When an EMO technique attempts to generate the set of nondominated solutions for a problem, then DMAR-related uncertainty needs to be reduced. Solutions generated by an EMO technique should be robust against perturbations caused by the DMAR. In this paper, we focus on the DMAR as a source of uncertainty and present two new types of robustness in MOP. In the first type, dominance robustness (DR), the robust Pareto solutions are those which, if perturbed, would have a high chance to move to another Pareto solution. In the second type, preference robustness (PR), the robust Pareto solutions are those that are close to each other in configuration space. Dominance robustness captures the ability of a solution to move along the Pareto optimal front under some perturbative variation in the decision space, while PR captures the ability of a solution to produce a smooth transition (in the decision variable space) to its neighbors (defined in the objective space). We propose methods to quantify these robustness concepts, modify existing EMO techniques to capture robustness against the DMAR, and present test problems to examine both DR and PR.

To solve two-dimensional (2D) rectangular packing problems, we introduce a new spatial method based on the discretization of the container into a grid of cells with predefined resolution. Before an item is added, grid cells are checked whether they can accommodate the item. If an appropriate empty cell cluster is found, the item is added and moved towards the bottom-left corner of the container. This placement and sliding method is supplemented by a heuristic that orders the items according to descending size. Order and rotation of items can be improved by hybridizing the heuristic with a genetic algorithm (GA) in which a population of order-rotation chromosomes is evolved. The method is tested on 47 benchmark problems and compared to other methods in the literature. This shows that it is fast and performs very well in finding close to optimal problem solutions. Particularly for large problem sizes, it outperforms some of the currently leading methods, such as heuristic recursive (HR). The hybridization with the GA meta-heuristic results in further performance improvements.

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and so

The need for guiding model formulation of normative social systems in support of a digital ecosystem is introduced. Normative social systems improve the understanding of computational social processes in simulation and experimentation, and provide support for digital ecosystem developments. However, a successful simulation requires the appropriate implementation of a conceptual model. It is proposed that an heuristic formalism of agents, networks and environments, complements the conventional creative approach to model formulation by guiding the formulation of conceptual models via abstract components and facilitate interface with other components in a digital environment.

Over the years, we have been applying multi- objective evolutionary algorithms (MOEAs) to a number of real- world problems, solving multi-objective optimization problems (MOPs) in the real world faces a number of challenges including when to terminate the algorithm. This paper addresses this challenge by introducing what we call a "stability measure". We use this measure to estimate when to stop the multi-objective evolutionary search. For the proposed measure, the non-dominated set obtained by an MOEA will be tested under local variability in the decision variable space. A non-dominated solution found by the MOEA will be assigned a stability value, which corresponds to the number of solutions within the neighborhood that dominate it. Obviously, if the found non-dominated solution lies on the Pareto optimal front (POF) then there cannot be any such dominating solutions in its neighborhood. The average of stability values assigned to all non-dominated solutions will be used as the stability value for the set. In order to validate the proposed measure, we carried out measurements on the obtained non-dominated sets from two MOEAs (NSGA-II and SPEA2), and two other well-known algorithms hill-climber, and a random-walk. We use random- walk in order to create a baseline for judging the performance of the algorithms, where we expect the highest level of variations to occur. To apply this measure at each generation, it incurs additional cost that does not contribute to the evolutionary search per se. This motivated us to add this measure as a local search operator. In this way, the local search operator plays two roles: (1) it attempts to find better solutions than the ones we have in the population; and (2) it acts as a stability measure for the evolutionary search. The results confirmed the usefulness of the proposed measure and algorithm.

In this paper, we introduce the air traffic operations and management simulator (ATOMS), which is an air traffic and airspace modeling and simulation system for the analysis of free-flight concepts. This paper describes the design, architecture, functionality, and applications of the ATOMS. It is an intent-based simulator that discretizes the airspace in equal-sized hyper-rectangular cells to maintain intent reference points. It can simulate end-to-end airspace operations and air navigation procedures for conventional air traffic, as well as for free flight. Atmospheric and wind data that are modeled in the ATOMS result in accurate trajectory predictions. The ATOMS uses a multiagent-based modeling paradigm for modular design and easy integration of various air traffic subsystems. A variety of advanced air traffic management (ATM) concepts that are envisioned in free flight are prototyped in the ATOMS, including airborne separation assurance (ASA), cockpit display of traffic information (CDTI), weather avoidance, and decision support systems (DSSs). Experimental results indicate that advanced ATM concepts make a sound case for free flight; however, there is a need to investigate and understand their complex interaction under nonnominal scenarios.

Many complex systems, whether biological, sociological, or physical ones, can be represented using networks. In these networks, a node represents an entity, and an arc represents a relationship/constraint between two entities. In discrete dynamics, one can construct a series of networks with each network representing a time snapshot of interaction among the different components in the system. Understanding these networks is a key to understand the dynamics of real and artificial systems. Network motifs are small graphs-usually three to four nodes-representing local structures. They have been widely used in studying complex systems and in characterizing features on the system level by analyzing locally how the substructures are formed. Frequencies of different network motifs have been shown in the literature to vary from one network to another, and conclusions hypothesized that these variations are due to the evolution/dynamics of the system. In this paper, we show for the first time that in strategy games, each game (i.e., type of dynamism) has its own signature of motifs and that this signature is maintained during the evolution of the game. We reveal that deterministic strategy games have unique footprints (motifs' count) that can be used to recognize and classify the game's type and that these footprints are consistent along the evolutionary path of the game. The findings of this paper have significance for a wide range of fields in cybernetics.

Evolutionary games are used to model and understand some complex real world situations in economics, defence, and industry. However, different games are usually studied independently and in isolation of each other. Notwithstanding, in real world situations, an agent is involved in multiple games simultaneously and her action in one game influences her utility in the others. This situation is far more complex when the utility functions for the different games are in conflict with each other. In this paper, we propose for the first time an analysis for the interaction between the iterated prisoner's dilemma and the minority games. We empirically explore the relationship and clarify the mathematical conditions under which the Minority game won't change the dynamics of the iterated prisoner's dilemma game.

Evolutionary games are used in understanding the complex dynamics of real life situations. The iterated prisoner's dilemma game with exit option was used in modeling strategic alliances. The model was solved as an optimization problem, and several observations were considered as to how partners are behaving in the alliance, and what factors are affecting the alliance's cooperation level. In this paper we introduce an evolutionary version of the iterated prisoner's dilemma game with exit option to better understand alliance activity and investigate whether the observations from the mathematical solution continue to hold in the evolutionary environment. The results reveal that in some cases there is a significant difference in the evolutionary dynamics from that predicted by the mathematical solution. The mathematical model was found to be inadequate for alliances which use history information to make their future decisions.

Planning future force structures is usually associated with a high, but difficult to quantify, risk factor. Among many other reasons for the importance of this planning process, the defence industry requires the military establishment to communicate their decisions on the capabilities needed in the future. This communication enables the industry to shape their R&D programs and tailor their production plans. Overall, the decision maker needs to anticipate the state(s) of the environment across a relatively long time frame. The process of anticipation is surrounded with many risk factors. Moreover, transforming a force structure is not a single-step process. Intermediate force structures need also to lake into account threats in the medium future. This paper presents a temporal risk assessment methodology for planning future force structure. The methodology relics on constructing a topological structure of interfiled into force structures with transitions based on the proximity of these structures to each other and budget constraints. The path with minimal maximum risk is then identified using a dynamic programming min-max path finder algorithm. The methodology is demonstrated with two simple examples

We describe a forensically-motivated, semi-automatic tool, which yields steady-state locations and cepstral parameters for contemporaneous and non-contemporaneous recordings of the five vowels in spoken Japanese. Using the notion of spectral prototype obtained from the mean cepstrum of a vowel’s highenergy interval, coupled with the peak-sensitivity property of the index-weighted cepstral distance, the tool is able to find steady-state intervals that are the least-phonetically deviant from the prototype. In addition to the consistency in steadystate location afforded by this approach, non-contemporaneity is taken into account by seeking the minimum deviation across all recordings. The overall design of the tool draws its efficiency from the interactive ability to quickly alter settings and visualize intermediate results in the time and frequency domains.

This paper describes a methodology, and the results stemming from it, for analysing the dynamic encoding of speaker identity and dialect in prosodic parameters. A method based on employing properties of the well known Dynamic Time Warping (DTW) algorithm’s path of best match allows the separation of purely dynamic from static properties of acoustic parameters and hence their evaluation as to dynamic encoding of speaker characteristics. Nineteen adult speakers of Australian English were recorded uttering a set of four sentences on five separate occasions over a period of at least one week. The prosodic parameters F0, short-time energy, zero crossing rate and voicing were extracted for all data and analysed as to their dynamic encoding of speaker identity and dialect. Discriminate analysis (for speaker identity) and correlation analysis (for speaker dialect) analysis showed higher dynamic encoding of identity (75%) and dialect (0.58) than static encoding (55 % and 0.45 respectively). Normalisation of all parameters into the range 0—1 reduced discriminate and correlation scores to 70 % and 0.54 respectively. Contrasting the warp path parameters with the more conventionally employed DTW distance showed that the warp path parameters better measured speaker identity (72 % versus 54%) and speaker dialect (0.56 versus 0.31) encoding. Individual analysis of the prosodic parameters shows a far higher encoding of identity and dialect in F0, though all four parameters encode dialect and identity. 1.

Speech understanding can be viewed as a problem of translating the input natural language of speech recognition results into an output semantic language. This paper describes automatic acquisition of a language model for translating natural language into semantic language from a text corpus using a stochastic method. The method estimates the co-occurrence probabilities of input and output grammar rules as a translation language model. Since the amount of text is limited, estimating a reliable language model is difficult. Therefore, we propose a method of concisely modeling input and output grammars in order to estimate a reliable translation model. Our method is shown to be effective by experiments using the ARPA ATIS task.