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Procedia Computer Science

Procedia Computer Science 00 (2013) 000–000

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Conference on Systems Engineering Research (CSER’13) Eds.: C.J.J. Paredis, C. Bishop, D. Bodner, Georgia Institute of Technology, Atlanta, GA, March 19-22, 2013.

Augmented Cognition in Human–System Interaction through Coupled Action of Body Sensor Network and Agent Based Modeling Siddhartha Agarwal, Cihan H. Dagli Smart Engineering Systems laboratory Engineering Mangement and Systems Engineering Department Missouri University of Science and Technology,Rolla,65401,USA

Abstract This research proposes a conceptual model to make significant progress in augmented cognition. We intend to design a generic architecture to improve the collective situational awareness (SA) of a team of soldiers on the battlefield during combat effects, and cultural communications. It also establishes a training outline for future landscapes of a similar nature by providing convincing feedback. It will serve as architecture of a model, based on the current laboratory framework. This involves data analysis of the feedback from the neurophysiological body sensors (heart rate, skin temperature, breathing rate and others) placed on a wearable immersive training vest. It includes gesture tracking gloves and fixed spatial locators. Approximately 15 soldiers will be incorporated into the study. Various training scenarios will be designed to capture all possible body perturbations, movements, and interactions. A similar agent-based model (ABM) of the training environment will be created to augment the soldier’s cognitive competencies. ABM will integrate social systems interaction complexity into the decision making capability of the soldier in real time. Automated feedback can be provided using the model developed in the event of any social faux pas via gesture tracking and monitoring cognitive capacity. Model can help discover novel talents and render knowledge during training scenarios.

© 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Georgia Institute of Technology. Keywords: Agent; Modeling; Cognition; neurophysiological; training; sensor; biometric

* Corresponding author. Tel.: +0 573 647 9125; fax: +0 573 341 7328. E-mail address: [email protected], [email protected].

Author name / Procedia Computer Science 00 (20123) 000–000

1. Introduction Augmenting human intellect enhances man’s ability to approach a complex situation. Augmented cognition enables the human, gain conception for adaptation to his particular needs and derive explanations [1]. The United States army has a presence currently in approximately 150 countries. These soldiers need verbal, cultural and situational awareness skills that will allow them to complete missions. To address operational adaptability, the Army must adapt its training methodology [2].The goal is to augment the capability of how soldiers observe, cognize, and reason as information streams are received during both training and real battlefield scenarios. More importantly soldiers as groups need year round training and situation centric support against the continuously adapting adversary. Soldiers should train in realistic scenarios and new training paradigms that integrate socio-technological capabilities and introduce conditions of uncertainty. Appropriate feedback to mitigate the obscurity in real life should be provided simultaneously during training. Objective of this research is to enhance the capabilities of the existing laboratory setup at Missouri University of Science & Technology (Missouri S&T). The setup is explained in detail in section 1.3. This research will contribute to the development of technology-based training of the future. A brief overview of the system architecture is presented in the following paragraph. The proposed conceptual architecture is a system of systems (SoS) construct comprised of several complex adaptive systems (CAS), such as humans and agents which are evolving and adapting in space and time constantly. An overview of the SoS architecture is illustrated in Fig. 1. A SoS architecture always involves a meta architecture. The meta architecture includes trainees and agents as evolving components whereas hardware, software and the surrounding environment are the core components of the system. The core components of a system remain unchanged over time, whereas evolving components are both changing and adapting. SoS architectures are difficult to model in terms of rigid equations and preconceived ideas. The training scenarios are modeled as multi-agent systems (MAS) to meet the need of generating automated feedback. When the soldier/trainee attempts to attain the objective defined in the mission both physiological information, along with movement, gestural, and behavioral data, can be received via sensors. This data is then integrated into this system through agent based models (ABM). The command center can respond by sending an emergency signal or an appropriate feedback in real time. Such inputs on surroundings and personal health can provide deeper insight of the situational awareness (SA). Inputs can help us evaluate performance and give a better and timely feedback. This paper investigates how a conceptual model of an architecture can be feasible with capability of augmented cognition and decision support system. Department of Defense (DoD) and other stakeholders can utilized the architecture to plan missions for unforeseen battlefield scenarios, social and cultural interactions in foreign lands. Research is being conducted to develop operational scenarios which capture the complexity of the system. Later we can generate data for modeling them in agent based environments. Strategically, this research will serve to both identify and bridge the potential gaps in both simulation and immersive training environments. The suggested model of an architecture combines in a sophisticated way body sensor network, agent based modeling, and multi-hop network mesh protocol. The protocol is developed at Missouri S&T for wireless communication.


Figure. 1. System overall architecture (Operational View) [3]

1.1. Seminal Work The prime motive for developing an integrated approach to augment human cognitive capability began in 1945[4]. The idea was given a more concrete shape by Licklider [5]. A procedure is needed that allows, in real-time, allocation of tasks between man and machine. This allocation is based on the varying scenario of the system and known as adaptive automation (AA). It was first suggested by Scerbo [6] in 2005 and then by Kaber [7]. AA is described as the control of function allocations between a machine and a human operator. These interactions are based on states of the collective human–machine system. Quicker, more accurate ways of receiving information regarding the fundamental parameters of human behavioral evolution and fluctuations are required, however, for AA to work. This information can be accessed through neurophysiological measures. These measures have an advantage over the behavioral observations made through naked eye [8]. Both stress and fatigue are useful measures because they can affect the performance of not only cognitive but also motor driven tasks controlled by the motor command neurons in the brain [9]. 1.1.1. Immersive Training and Teamwork The army realizes the significance of assessing cognitive states through models. The use of physiological measures remotely obtained during combat or training movements, such as heart rate, skin temperature, breathing rate, body acceleration and body posture can serve that purpose [10]. Dylan Schmorrow director of the Human Performance, Training, and Bio-Systems Research Directorate has a high level of confidence in the system generated from the coupling of human-computer interactions controlled via physiological operator cognitive state


measures. Dylan believes this system will have unmatchable powers and would prove to be the most formidable opponent in wartime situations [11]. Defense Advanced Research Projects Agency (DARPA) conducted an experiment known as the Technical Integration Experiment (TIE) [12, 13]. The objective was to identify the most important physiological measures capable of detecting changes in cognitive activity for a supervisory control task. Cognitive state gauges (CSGs) such as electroencephalogram (EEG), functional near-infrared (fNIR), and body posture measures, as well as input device measures (mouse pressure and mouse clicks) were used. CSGs acted as metrics for measuring change in cognition of the soldiers. TIE successfully demonstrated the ability to combine multiple sensors and collect real-time data in a command and control-type decision-making task. Another important aspect is teamwork. The tasks within a soldier missions rely, to a large extent, on the performance of the unit as a team. Thus, a need to raise the teamwork capability arises. Salas et al. [14] suggested strategies involving a unification of information, practice, and feedback together in the right sequence, can greatly enhance the team’s performance. 1.1.2. Simulating Reality The training practices commonly used have reduced capability. They lack in engaging trainees in higher value activities such as continuous improvement through feedback. One must have knowledge of the surrounding environment to either implement real time feedback from the command center or submit an automated response relayed to the soldier. Situational awareness (SA) has become increasingly accepted as the most important aspect of decision making in human teams [15]. SA has more relevance in cases where continuous exchange of information and multi-tasking is carried on. SA is the ability to both identify and process critical pieces of information on the surrounding environment. SA information about an individual’s environment can be used to grasp its current and future status [16]. SA of the training environment and its participants can be captured to a large extent by having real time sensory information of major physiological parameters such as heart rate, body temperature, respiration rate, blood oxygen levels, stress, gestures and posture etc. 1.2. Advantages of Utilizing the proposed Conceptual Architecture Generic predictions based on observables of both the decision and its consequences correlate little or none to the underlying cognitive process. The ultimate goal of monitoring both the physiology and behavior of the soldiers is to enhance SA. Within this context, SA can be related to not only what but also how is the team doing as it attempts the mission. Few contemporary systems have tried to model SA in an unambiguous fashion. SA will be captured through Biometric Data Collection, gesture recognition glove and feedback System developed here at MS&T. Another potential gap identified by Cummings [17] as an argument to TIE experiment is the ability to make consistent predictions in real time after receiving the sensory information in a SoS environment. The ABM will help in modeling the scenario with the sensory information received. It can then forecast according to that information. Soldiers are seldom informed of another’s performance. Team members should know the role each one is going to play in the scenario and how they performed [18]. This MAS will help evaluate both the team and individual performances. Skill can be measured as adaptability, the effective use of both equipment and resources, coordination in overall mission achievement, and improvement with feedback. Attitude can be comprehended as an internal state that influences a soldier’s choices and decisions under specific situations. The training reinforced by physiological monitoring and concurrent ABM forecasting can either fosters a positive attitude or boost morale through real-time feedback. Trainee acknowledges the backup for all upcoming situations and feels confident. The personnel tracking system allows trainees to remain aware of each other’s relative position within the scenario at all times. The compact controller (see Fig. 2b) can serve as a portable device able to act as a memory aid. It can monitor ammunition and spontaneously call for replenishment. This training methodology will also aid in post training sessions and debriefing. They play a crucial role in the training development and overall performance enhancement of the soldier. This again is not emphasized enough in previous training outlines.


1.3. System Physical Architecture Description This section describes in detail the current laboratory set-up at Missouri S&T. The immersive training system is designed in a closed room with an approximate area of 1200 sq. ft. The setup consists of:  a haptic feedback vest developed by Missouri S&T students [19],  a control module that interacts with the vest,  a communication protocol developed at Missouri S&T that incorporates multi-hop networking,  location tracking system,  a gesture tracking glove, and  a remote ambulatory physiological monitoring device for the soldier . The immersive vest system (see Fig. 2b) is primarily comprised of a body or civilian armor, a t-shirt with 16 tactors. These tactors are placed in accordance with the most sensitive areas of the body. A harness is developed for remote monitoring of vital signs. This vest can be used to communicate information to the trainee through the vibration of tactors placed throughout the vest in different patterns. Each pattern represents either a different message or a piece of information. The vibration motor placed inside the sensor acts as an actuator, indicating the location of its occurrence. The controller is worn by all trainees participating in the scenario. The controller communicates to a remotely located computer system via the self-organizing ad hoc network developed by a team of Missouri S&T students and faculty. The controller’s touch screen can both receive visual text feedback and send acknowledgment by the soldier. The personnel tracking system is a multiuser, inter-room position tracking technology. It uses Radio Frequency (RF) triangulation algorithm and N nodes to estimate the location in the grid. The RF triangulation calculates a user’s location based on the detected signal strength of nearby access points. The system tries to reliably track both all individuals within the room as well as their mobility within a time-scale. The system can track a maximum of 15 people. The location information is conveyed to the training control and data collection center via the controller on the trainee’s body. This controller also acts as a base node for the multi-hop mesh network present in the training room. Physiological monitoring device to be used in training can be worn as a chest strap. It is capable of transferring information from the sensors to the on body controller and then to a software analytical system. These sensors measure the following:  Heart  ECG  Breathing  Temperature (skin)  Activity (e.g., standing, walking or running)  Posture (e.g., either laying or standing) This sensor data gives the commander, safety personnel and medical monitoring personnel the actual, real-time status of personnel deployed downrange. The gesture tracking glove provides information on movement (e.g., position of the finger and wrist). This information is useful for scenarios in which the soldier commits a social faux pas during a cultural interaction. The person in the remote control room can evaluate his action and render suitable feedback. Both real-time monitoring and reporting could enhance the longevity of soldiers making their mission more efficient. The setup presented here satisfies the need.


Fig. 2. (a) Laboratory setup (b) T-shirt having the plates inserted in pockets sewed on itself, hand gesture tracking glove and Central Compact Controller (also connected to the t shirt through serial bus) (c) Vibrating Motor placed on specially designed composite plate.

Figure provides a visual of the laboratory setup. The visual presents two immersive wearable training vests. A compact controller mote alongside each of them is also presented. These devices are carried by the trainee throughout the training scenario. Missouri S&T Mote (wireless self-contained, smart sensor, and communication device) are placed at a distance of 0.3-9.5 meters from each other. It is depicted that the Motes are interconnected via a self-organizing mesh network. This network uses a multi-hop networking protocol. The protocol, developed at Missouri S&T is designed to minimize energy usage while assuring quality of service. Protocol is accomplished using inexpensive, low power radios built into the controller. The network’s protocol is able to relay information to either the central controller or the command center. The command center is stationary, providing feedback according to the information received. Figure 2b is a zoomed in view of the training vest. This vest has an array of pockets on the torso. These pockets contain vibrating haptic actuators (tactor motors) to provide a non-intrusive, salient, real-time feedback and information transfer. The tactor motors are mounted to specially designed composite plates to enhance their perceptibility. The gesture tracking glove shown is used to capture and assess proper arm/hand gesture discipline. A process using Kinect sensors was developed to “train” the system to detect improper gestures. The compact controller is comprised of inexpensive shelf hardware and a small touch screen interface. It has 16 high current output channels for the tactor motors. It has 6 analog to digital inputs for physiological monitoring sensors. Laboratory setup plays a critical role in simulating complex systems. Moreover it gives an opportunity to visualize the outcomes from simulations and match them with immersive training experiences and vice versa. The next section describes the modeling based prediction technique employed at the command center to give automated feedback to the trainee. 1.3.1. Modeling and simulation technique for the proposed architecture Breaking a complex system down to its individual components and then analyzing its behavior as a summation of the conduct of its constituents is often difficult. It is difficult because the complex system has acquired certain capabilities as a result of interactions between the components. These capabilities cannot be attributed to any of its components alone. Complex adaptive systems are exceedingly sensitive to their initial conditions [20]. When interdependent agents are adaptive and evolving their interaction can generate a complex adaptive system [21]. ABM is an attempt to study and analyze complex adaptive systems. The first such attempt was made at the Santa Fe Institute [22]. The goal of this study was to pursue research on highly complex and interactive systems.


The word “agent” has been adopted from economics. Agents are governed by a collection of stimulus responsible rules. Rules are a method to describe the agent strategies in the environment they inhabit. The objective of ABM is to determine how both the communications and diverse activities of individual agents can create not only organization but also pattern [23]. Hence an ABM approach is chosen to identify those differences between trainees that are relevant to simulation learning environments. It is achieved by incorporation of sensory, location and gestural information into the agent’s environment. Various tools can generate an ABM environment. These software range from freeware to paid software [24]. The aptness of using an ABM model for this application area can be summarized in the following properties that define it:  ABM is able to demonstrate the traits of the complex system in a changing phenomenon;  Agents are interdependent;  ABM is supple enough to adjust [25].

1.3.2. Benefits of Simulation-Based Training All of these attributes combined give rise to various emergent patterns that evolve in the course of simulation time. Emergence occurs in behavior, properties, and structure. These patterns are then analyzed to comprehend both the environmental conditions and agent characteristics that lead to such scenarios. Furthermore subtle or marked changes to the constituents of the complex system in its signature characteristics along with agent behavior changes could be done. These changes can record other manifestations evolving out of the same basic entities. Each human and non-human being modeled as an autonomous agent, can help foretell the emerging collective behavior. Agents not only prove to be good counterparts to the real beings but also agent based models, are successful in the endeavor to generate “would-be worlds” [26] in a computer. Neural networks, reinforcement learning, genetic algorithms, and other computational intelligence techniques are used to generate strategies for agents. As previously described a multitude of training scenarios can be used to train the ABM. This ABM thus acquires the capacity to generate a response in a situation it either has previously encountered or is similar to. In the actual training session and/or battleground ABM generates an automated response to both cultural and combat faux pas. Additionally ABM is able to integrate the cognitive ability, previous experience, and motivation of individual trainees. These traits have been shown to influence the learner control inherent in simulation-based training [27]. MAS have been employed in military combat simulations to demonstrate scope of teamwork which includes RETSINA [28] and CAST [29].

1.4. Conceptual Model Typically when simulation systems emulate human decision processes, they fail to consider the major aspects of physiological information. ABM attempts to bridge the gap, by transforming incoming information into meaningful and precise predictions of future states, in a dynamic command and control environment. ABM techniques will be utilized to develop artificial cooperative intelligence. This intelligence emerges from the association and competition of many individuals and appears as harmonious decision making on the macro-level. ABM will have agents with different levels of cognitive skill such as novice, advanced beginner, competent, proficient, and expert to relate proficiency levels of soldiers before they start training. Our purpose is to combine immersive and simulation based training to help create a realistic practice environment for military tasks. The ABM both learns and improves its predictive ability with each training session. ABM additionally helps to debrief the team of soldiers of their performance by summarizing individual and group performance quotients. This architecture gives the ability to alter the scenario in real time and the ABM can be easily modified to include that change. Training simulations are not designed with a thrust on personal attributes. Each individual, however is unique enough (has characteristic differences in both physical and behavioral traits) to modify the success of simulation-based training. This research takes this facet into account, giving the training process a perspective centered on the individual trainee. ABM provides a stronger esprit de corps among soldiers fighting for a common


cause. Measuring team performance in complex environments is difficult in general. ABM is the language that can capture the complexity of systems. It is proposed that soldiers in the training environment are modeled as 2-d agents able to move in the provided space individually and towards each other. The agents movements are governed by the data received from the spatial locators. Similarly, both the gesture tracking glove and the physiological sensors data is used to model behavioral qualities. Each agent is governed by data, from the corresponding soldier, through immersive vest. The simulated data can be compared with empirically collected data for model validation purposes. Simulations can be used to explore the range of potential outcomes. Simulations can be used to drive new theory development via the simulation-based experiments. The main objective is to identify swiftly the class or type of problem the soldier or its team is encountering. Once identified map or direct the soldier towards the most suitable solution to the problem. This mapping and/or identification of the pattern are then relayed to the team. Both the immersive training vest and the controller not only communicate data but also display it on the compact controller screen. This methodology gives accurate performance assessments and feedback simultaneously to make corrections. 1.5. Future Directions in System of System Architecture Design and Training This conceptual structure is an attempt to present a system architecture for training methodologies which qualifies for current and future technological innovation in the field of model based systems engineering. Intent is to develop an architecture that is modifiable, adaptable, and resilient to the external stimuli. Training techniques currently used are identified and reviewed. The aim is to fill the potential gap between them and the need of the hour. Both the theoretical concept and the existing laboratory setup used should make simulation based training more economical, productive, effective, adaptable, and interoperable. This research lies on the periphery of training for the military. It aims to go beyond the latest techniques and innovations. The approach includes situational awareness, communication infrastructure, movement, and viability of mission oriented objectives and endeavors. Additional physiological measures that have emerged prominent are sleep deprivation, hydration and body mass index. Since army missions are extending to increasingly long and arduous hours [30] further research is being conducted to include these parameters in our architecture. This will make for a healthier more competent soldier.

Acknowledgements This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) under Contract H98230-08-D-0171. SERC is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Department of Defense.

References 1. 2. 3. 4. 5. 6.

Engelbart, D. (2001). Augmenting human intellect: a conceptual framework (1962). PACKER, Randall and JORDAN, Ken. Multimedia, SRI Summary Report AFOSR-3223. "Active Duty Military Personnel Strengths by Regional Area and by Country (309A)". United States Department of Defense. December 31, 2011. Retrieved December 4, 2012. National Guard (2009, February 3). Retrieved September 11, 2012, from http://www.nationalguard.mil/resources/photo_gallery Bush, V., "As We May Think." The Atlantic Monthly, 1945. 176(July): pp. 101-108. Reprinted and discussed in interactions, 3(2), Mar 1996, pp. 35-67 Licklider, J. C. R. (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4–11. Scerbo, M. W. (1996). Theoretical perspectives on adaptive automation. In R. Parasuraman & M. Mouloua (Eds.), Human performance in automated systems: Theory and applications (pp. 37–64). Mahwah, NJ: Erlbaum.

Author name / Procedia Computer Science 00 (20123) 000–000 7.

8.

Kaber, David B., Melanie C. Wright, Lawrence J. Prinzel, and Michael P. Clamann. 2005. “Adaptive Automation of Human-Machine System Information-Processing Functions.” Human Factors: The Journal of the Human Factors and Ergonomics Society 47 (4) (December 1): 730–741. Schmorrow, D.D.; Kruse, A.A.; , "DARPA's Augmented Cognition Program-tomorrow's human computer interaction from vision to reality: building cognitively aware computational systems," Human Factors and Power Plants, 2002. Proceedings of the 2002 IEEE 7th Conference on , vol., no., pp. 7-1- 7-4, 2002.

9.

TREATY, N. (2010). Real-Time Physiological and Psycho-Physiological Status Monitoring, 323(July). Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Real-Time+Physiological+and+PsychoPhysiological+Status+Monitoring#0 10. Bolton, A., Campbell, G., & Schmorrow, D. (2007). Towards a Closed-Loop Training System : Using a Physiological-Based Diagnosis of the Trainee’s State to Drive Feedback Delivery Choices, 409–414. 11. Cohn, V. Joseph., The PSI Handbook of Virtual Environments for Training and Education [3 volumes], Ed. Dylan Schmorrow, Denise Nicholson.Westport: ABC-CLIO, Incorporated, 2008. 12. St. John, M., Kobus, K. L., Morrison, J. G., & Schmorrow, D. (2004). Overview of the DARPA Augmented Cognition Technical Integration Experiment. International Journal of Human Computer Interaction, 17(2), 131–149. 13. Endsley MR. Situation awareness in aviation systems. In: Garland DJ, Wise JA,eds. Handbook of aviation human factors. Human factors in transportation.Mahwah, NJ: Lawrence Erlbaum Associates, 1999:257–76. 14. Salas, Eduardo, and JA Cannon-Bowers. 2001. “The Science of Training: A Decade of Progress.” Annual Review of Psychology. 15. Reynolds C. (1987) Comput. Graphics 21, 25-34. 16. Endsley, M.R., Selcon, S.J., Hardiman, T.D., & Croft, D.G. (1998). A comparative evaluation of SAGAT and SART for evaluations of situation awareness. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (pp. 82–86). Santa Monica, CA: Human Factors and Ergonomics Society. 17. Cummings, M L. 2009. “Technology Impedances to Augmented Cognition”: 25–27. 18. Cannon-Bowers, J. A., Tannenbaum, S. I., Salas, E., & Volpe, C. E. (1995). Defining competencies and establishing team training requirements. In R. A. Guzzo & E. Salas (Eds.), Team effectiveness and decision making in organizations(pp. 333-381). San Francisco: Jossey-Bass. 19. Bodenhamer, A.S.; Dagli, C.H.; Corns, S.M.; Guardiola, I.G.; , "Development of an immersive training vest," Systems and Information Design Symposium (SIEDS), 2012 IEEE , vol., no., pp.173-177, 27-27 April 2012 20. Mills, Alan. “Complexity Science:An Introduction (and Invitation) for Actuaries.” Society Of Actuaries Health Section (Version 1). 21. Holland, John H.; (2006). "Studying Complex Adaptive Systems." Journal of Systems Science and Complexity 19 (1): 1-8. http://hdl.handle.net/2027.42/41486 22. W.B. Arthur, S.N. Durlauf, D.A. Lane (Eds.), The Economy as An Evolving Complex System II, Proceedings Vol. XXVII, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, Reading, MA, 1997. 23. Allan, R. (2009). Computational Research into Complex Systems. Retrieved from http://en.scientificcommons.org/46189810 24. R.J. Allan Survey of Agent Based Modelling and Simulation Tools Daresbury Laboratory DL-TR-2010-007 (2010) 25. E. Bonabeau Agent based modeling: Methods and techniques for simulating human systems Proceedings of the National Academy of Sciences of the United States of America 99 (2002) 7280-287 26. Casti J., (1997) Would-Be Worlds: How Simulation Is Changing the Frontiers of Science (Wiley, New York). 27. Ioerger, T.R., He, L., Lord, D., and Tsang, P. (2002). Modeling Capabilities and Workload in Intelligent Agents for Simulating Teamwork. Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society, 482-487 28. Paolucci, M., Kalp, D., Pannu, A., Shehory, O., Sycara, K.: A planning component for RETSINA agents. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757. Springer, Heidelberg (2000) . 29. J. Yen, J. Yin, T. Ioerger, M. Miller, D. Xu, and R. Volz. Cast:colaborative agents for simulating teamworks. In IJCAI’2002, pages 1135–1142, 2001. 30. Lieberman, H.R., Bathalon, G.P., Falco, C.M., Kramer, F.M., Morgan, C.A. and Niro, P., (2005). Severe decrements in cognition function and mood induced by sleep loss, heat, dehydration, and under-nutrition during simulated combat. Biological Psychiatry, 57:422-429.