When and Where Will AI Meet Robotics? Issues in Representation

Articles When and Where Will AI Meet Robotics? Issues in Representation

Ruzena Bajscy and Edward W. Large

Because perception-action systems are necessarily tems and agents interacting with the physical constrained by the physics of time and space, world, but AI deals mostly with abstract prob- robotocists often assume they are best described lems that lend themselves to symbolic repre- using differential equations, a language that is spe- sentations. cialized for describing the evolution of variables The environment: Roboticists seek to that represent physical quantities. However, when design systems that function in physical envi- it comes to decision making, where the represen- tations involved refer to goals, strategies, and pref- ronments that are always changing and intrin- erences, AI offers a diverse range of formalisms to sically unpredictable. Software agents generally the modeler. However, the relationship between operate in human-designed worlds where one these two levels of representation—signal and can have some measure of control over change symbol—are not well understood. If we are to or at least an a priori knowledge of the possibil- achieve success in modeling intelligent physical ities. agents, robotics and AI must reach a new consen- The tools: AI more commonly uses discrete sus on how to integrate perception-action systems mathematics, but robotics and machine per- with systems designed for abstract reasoning. ception make use of continuous mathematics. These tools also differentiate the typical educa- n the early days of AI, robotics was an inte- tional background that characterizes the two gral part of our research effort. All our major areas: AI has more computer scientists, but IAI laboratories had research programs in robotics has more electrical and mechanical robotics in the late 1960s and early 1970s. engineers. However by the 1980s, robotics had taken its The evaluation criteria: AI researchers seem own course separate from the core activities of to value novelty more, solving “hard” prob- AI. One might argue that such a split was lems, showing existence of solutions, and so inevitable, a natural result of specialization in forth. Robotics, however, follows traditional a rapidly growing and maturing field such as engineering evaluation criteria: efﬁciency, reli- ours, but in our pursuit of rational models of ability, accuracy of performance, and economy the mind, do we dare leave the body behind? of the solution. What is responsible for the divergence We admit that these divisions might be between these two ﬁelds that once were so inti- overexaggerated because in both fields, one mately intertwined? Can AI and robotics ever can find counterexamples to the previous be reunited, and if so, what would a new part- statements. Nevertheless, each of these points nership look like? At the core, we believe, is the speaks to the differences one encounters when ubiquitous issue of representation. There is an dealing with corporeal agents in the physical enormous difference between dealing with world versus software agents in cyberspace. physical systems that operate in our everyday Robotics concentrates most of its resources on environment and software systems that reside modeling perception and action. Often, differ- in various abstract worlds. This gap has led to ential equations are used to embody relatively divergence in many areas, including the fol- simple strategies for controlling hardware lowing: effectors based on sensory information. AI, The problems: Robotics problems entail sys- however, emphasizes planning and abstract

information and interacting physically with humans, they will be required to integrate more sophisticated perception-action capabili- ties with their abstract reasoning abilities. In addition, although each of these areas has been well studied, in robotics and AI respec- tively, the integration of perception-action systems with reasoning systems is less well understood. Thus, there is a great need to reconsider the relationship between AI and robotics.

The Problem of Representation We will explore the issue that we believe is key to this relationship, the issue of representation. Representation is critical, especially when one considers how to ﬁnd a description that is compact, yet expressive enough to enable the modeling of intelligent physical agents. What kind of mathematical tools are available to us? At the signal level, modeling of perception- action (reactive) behaviors can be anchored in differential equations and control theory, both linear and nonlinear. At the symbol level, higher-level control derives its models either from geometry (typically used in robotics) or from logics and rule-based systems such as are favored in the AI planning community. If time needs to be explicitly accounted for, then there are other tools available. At the signal level, time is implicit in the model of the reactive behaviors (for example, using differential equations). At the symbol level, discrete states are generally considered, and these can be modeled using discrete-event systems; tempo- ral logics; and, at an even higher level, ﬂuents. If uncertainty and disturbances must be modeled, then one must bring to bear stochastic Figure 1. The Autonomous Agent. models and probability theory; partially observable Markov decision models is one The TRC platform serves as a mobile base. A stereo camera pair mounted on the front of the rig is used for obstacle detection. A third camera such example. Finally, utility functions and mounted on a pan platform is used for target detection and tracking. cost-benefit trade-offs come to play in con- junction with game theory, optimization, selection of strategies, and complexity consid- erations. Examples of such approaches as they reasoning. For example, logical, grammatical, have been applied to robotics are presented in or other discrete formalisms are used to model Alami et. al. (1998); Clementia, Di Felice, and the complex operations involved in winning a Hernandez (1997); Cohn (1995); Russell and Subramanian (1995); and Sandewall (1994). chess match or parsing a sentence. Hybrid system approaches such as discussed in What seems clear is that as robotic agents Arkin (1998), Brockett (1993), Dickmanns are called on to perform increasingly complex (1997), Nagel and Haag (1998), Nerode and tasks, they will be required not only to react Remmel (1991), and Ramadge and Wonham flexibly in dynamically changing environ- (1987) combine discrete systems with lower- ments but also to make decisions, reason level control systems. However, the use of all abstractly, and change perceptual or behav- these mathematical tools is predicated on the ioral strategies. Conversely, as intelligent soft- assumption that label assignment (the seg- ware agents are required to operate more and mentation of the sensory or control signals) is more on human terms, responding to sensory performed externally to the system.

58 AI MAGAZINE Articles

Discrete-Event System Competitive Dynamic System

ψ < 0 Weight, w (attractor location)

Symbol Level State State ... (Decision) A ψ > 0 B ψ Bifurcation Point Discrete Event

Interface Θ˙ ΘΨ, Θ˙ Θ,,Ψ = fstate() = f ()W

1 Attractors Signal Level Ψ

, 0

(Control) φ () f Repellor −1 −π −π/2 0 π/2 π Behavioral Variable, φ

Figure 2. Two Approaches to Decision Making and Behavioral Sequencing. In the discrete-event⋅ system, the discrete states of a ﬁnite-state machine correspond to control of perception-action by a distinct control law (Θ = F (Θ, Ψ)). Transitions between states are governed by guard conditions on perceptual variables (for ψ state example, ). In the competitive dynamic system, each state variable (for example, w) controls⋅ the weighting of a task constraint at the signal level. Behavior is shaped directly by the competitive dynamic system, Θ = F(Θ, W, Ψ), as the symbol- level system activates and deactivates attractor and repellor contributions to the behavioral dynamics. As perceptual parameters (for example, ψ) change, bifurcations cause qualitative changes in perception-action behavior.

Signal and Symbol mobile platform equipped with a stereo camera pair used for obstacle detection; a third In our view, the main unsolved problem is camera mounted on a turntable for visual how to segment the continuous signal into tracking; and several computers used for pro- states, strategies, labels, and so forth, and how cessing sensory signals, generating control sig- to arbitrate among states for effective interac- nals, and making decisions. tion with the environment (for example, Shi The control system that models perception- and Malik [1998], Tari, Shah, and Pien [1997], action behavior transforms visual input into and Large, Christensen, and Bajscy [1999]). In control signals to enable the physical agent to the GRASP Laboratory, we have concerned carry out various navigation tasks. We use a ourselves with the problem of representation dynamic system approach proposed by Schon- in signal-symbol systems over the past several er, Dose, and Engels (1996). In this approach, years. One approach to this problem involves behavior is controlled by differential equations the study of intelligent physical agents, agents that describe the rate of change of behavioral that can operate in the physical world with all variables, such as heading direction and veloc- its uncertainty yet behave intelligently, mak- ity. At any instant in time, the values of these ing decisions about how best to perform sim- variables describe the agent’s behavior. Over ple and complex tasks in a range of real–world time, the dynamic system generates a series of environments. A picture of one such agent is values, controlling the behavior of the agent. shown in ﬁgure 1. It consists of a TRC LABMATE Our dynamic system has the form

FALL 1999 59 Articles

Ddt = F(,) (1) to rendezvous with an agent and then proceed where = [ ]T is a vector of behavioral vari- toward a target location. To perform such tasks ables, heading direction, and velocity and is a in a complex environment requires sequenc- vector of variables that represent perceptual ing several simpler perception-action strate- information, such as direction to the target gies. Building on the previous signal-level rep- and size of obstacles. An example of such a resentation, we have investigated two function is shown in figure 2 (bottom panel). approaches to modeling decision making and Three fixed points can be seen in the figure as sequence generation: (1) a discrete-event–system points where the value of f(,) is zero (that approach and (2) a competitive dynamic system is, d/dt = 0, so heading direction is fixed). If approach. There are three key differences the slope of the function around a fixed point between these two approaches: (1) the model is positive, the value of the behavioral variable of how the symbol level interfaces with the sig- is pushed away from this value by the action of nal level, (2) the model of how perception is equation 1; such an unstable fixed point is integrated into the decision-making process, Although called a repellor. If the slope of the function is and (3) the model of how decisions are cap- the dynamic negative, it is a stable fixed point, called an tured at the symbol level. attractor, because the behavioral variable is Before describing each approach in detail, control system pulled toward this value by the differential we summarize the differences between the two provides a equation. systems in figure 2. In the discrete-event– system The behavior of the agent is controlled by approach (Kosecka 1996), the symbol level great deal of the configuration of attractors and repellors: interfaces with the signal level by realizing dis- flexibility, it Desired actions (such as moving toward a tar- tinct behaviors as separate dynamic systems get) are modeled as attractors, and undesired (that is, fstate, middle left panel); these are con- can only actions (such as moving toward an obstacle) ceived of as elementary perception-action model one are modeled as repellors of the perception- strategies. At any particular time, the behavior action dynamic system. Task constraints deter- of the agent is governed by one of these equa- relatively mine the mapping from perceptual informa- tions. Decision making and sequencing are simple tion to behavioral attractors and repellors. If modeled at the symbol level using a finite-state perception- the task is to go to the desk, the action of mov- machine (FSM) (top left panel). Individual FSM ing toward the desk is modeled an attractor, states correspond to elementary signal-level action but other objects are considered obstacles behaviors; when in a particular state, behavior behavior (modeled as repellors). However, if the task is is governed by a corresponding signal-level to rendezvous with another agent, then the dynamic system. The arcs linking states are at a time. action of moving toward the other agent is an labeled with discrete events (for example, > Complex attractor, and the desk is treated as an obstacle, 0) that summarize perceptual information. The and moving toward it is to be avoided. Thus, perceptual system generates these events, tasks, viewed as a representation of a perception- which correspond to qualitatively different however, action behavior, this dynamic system incorpo- conditions in the environment. The occur- typically rates task knowledge and makes use of percep- rence of a specific event causes the switch from tual information. one state to another, modeling the decision to require the As the values of the perceptual variables execute a different perception-action behavior. execution of change, the attractor-repellor layout changes. Thus, sequences of behavior are generated by For example, if a target moves, the location of traversing the arcs, which is, in turn, governed sequences of the corresponding attractor will move as well, by the conditions on the current situation. behavior. thus providing behavioral flexibility—behav- The competitive dynamic system model is for- ior adapts to accommodate changes in the mulated entirely within the qualitative theory environment. An even greater measure of flex- of dynamic systems. Both signal-level control ibility is provided by bifurcations in the dynam- and symbol-level decision making are modeled ic system—qualitative changes in the layout of using differential equations (Large, Chris- attractors and repellors caused by changes in tensen, and Bajscy 1999). However, the com- parameter values. For example, when a new petitive dynamic system interfaces with the sig- obstacle comes into view, a repellor forms nal level differently than the discrete-event where there was no repellor before. system. Rather than defining multiple elemen- Although the dynamic control system pro- tary perception-action behaviors, a single mas- vides a great deal of flexibility, it can only ter equation is defined containing all possible model one relatively simple perception-action task constraints (middle right panel, figure 2). behavior at a time. Complex tasks, however, Then, each variable in the symbol-level system typically require the execution of sequences of controls the weighting of a task constraint at behavior. For example, one agent might need the signal level, such that the symbol-level sys-

60 AI MAGAZINE Articles

Α) goto

succ

fail

goto B) succ obst succ fail ε fail

Figure 3. Finite-State Machine (FSM) Models for Simple and Complex Behaviors.

A. An FSM for elementary behavior GoTo. The control law (fGoTo ) is repeatedly invoked in the next state until suc- cessful (arrival at the goal) or unsuccessful (for example, detection of a spurious attractor) termination. B. Finite-state model for a navigation behavior. Failure of GoTo is followed by the elementary behavior Escape. Once the agent clears the obstruction, GoTo is invoked again. This more complex behavior is able to handle a large variety of navigation situations. tem can activate and deactivate attractor and teresis, govern decision making and sequence repellor contributions to the behavioral generation. dynamics. Different behaviors are modeled as fixed points (stable weight configurations) in Discrete-Event Systems the competitive dynamic system. The environ- The discrete-event approach models elemen- ment determines the values of the system para- tary perception-action strategies as behavioral meters. As the perceptual information changes, atoms, and each elementary control law is parameters change, causing bifurcations in the associated with a state in a simple finite-state symbol-level system (top right panel, figure 2), machine. We define the composition operators modeling the decision to cease executing one for the FSMs by imposing some additional behavior and execute another instead. This structure. The set of final states of an elemen- approach differs from the discrete-event tary behavior is partitioned into a set of suc- approach because the properties of dynamic cessful and unsuccessful final states. By utiliz- systems, such as stability, bifurcations, and hys- ing these primitives, it is possible to build

FALL 1999 61 Articles

1 0.8

0.6 0.4 0.2 0 -0.2 -0.4

Weight for target for Weight -0.6 -0.8 -1 0 0.2 Advantage for Obstacles 1 0.4 0.8 0.6 0.6 0.4 0.8 0.2 1 0 Advantage for Target

B) 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 Weight for obstacles for Weight -0.6 -0.8 -1 0 0.2 Advantage for Obstacles 1 0.4 0.8 0.6 0.6 0.4 0.8 0.2 1 0 Advantage for Target

Figure 4. Bifurcations in a Competitive Dynamic System for Decision Making. Two bifurcation diagrams are shown, one corresponding to each dimension of a two-dimensional system for system navigation. It is γ γ assumed that tar,obs = obs,tar = 0.5. A. The state variable wobs determines the weighting of ftar. B. The state variable wobs determines the weighting of fobs. Four qualitatively different behaviors, corresponding to four ﬁxed points of the competitive dynamics, are shown.

62 AI MAGAZINE Articles models of more complex tasks, which are the competitive dynamic system, and as with described as sequences of behavioral atoms. By any dynamic system, bifurcations are caused using a task-specification language, complex by changes in the values of the system parame- FSMs are synthesized by sequencing simpler ters. The decision-making system uses two automata. types of parameter: (1) competitive advantage The FSM model of an elementary GoTo and (2) competitive interaction. These parame- strategy is shown in figure 3a. A signal-level ters are tied to perceptual information, so that perception-action behavior is repeatedly decisions are made on the basis of the environ- invoked in the next state until the agent reach- ment as sensed by the agent. First, each weight es the desired target location and makes a tran- has an associated competitive advantage that sition to the final state. If the strategy fails, the describes whether the corresponding task con- transition to the unsuccessful final state is straint (for example, move toward target, made. As an example of a composition of ele- avoid obstacles) is appropriate to the agent’s mentary behaviors, consider the problem of current situation. For example, if obstacles are moving to a target location while avoiding nearby, the Obstacles constraint will have a obstacles. In simple environments, one ele- strong competitive advantage, but if the target mentary perception-action behavior, GoTo, is also in view, the Target constraint also has a Intelligent might do the trick. However, in complex envi- strong advantage. The activation of both con- ronments with multiple obstacles arrayed in straints simultaneously corresponds to the ele- agents must difficult configurations, our agent might get mentary GoTo behavior of the discrete-event be capable of stuck in an area and never reach the target system earlier. Competitive interaction describes location (Large, Christensen, and Bajscy 1999). the extent to which each constraint is consis- bringing to We address this problem by adding an Escape tent or inconsistent other constraints. For bear a rich behavior that enables the agent to find its way example, if the agent finds itself enclosed in an variety of per- out of enclosures and other spatial traps. The area with the target just beyond, the competi- FSM for this behavior is shown in figure 3b. We tive interaction between the Obstacles and the ception-action assume that the fail signal to the GoTo behav- Target constraints would increase so that the strategies but, ior is generated whenever the agent detects an Target constraint would be deactivated tem- enclosure from which it must escape. When porarily, allowing the agent to escape from the at the same the fail signal is generated, the FSM enters the enclosure. Deactivation of the target con- time, reason- unsuccessful final state, and a transition is straint corresponds to the Escape behavior of made to the initial state of Escape. When the discrete-event system. ing and solv- Escape terminates (when the agent has cleared We can understand in detail how these para- ing problems the obstruction), the GoTo behavior resumes. meters interact to determine the behavior of to perform The navigation task terminates successfully the agent by constructing a bifurcation dia- when the agent reaches the target location. gram such as that in figure 4. The bifurcation both familiar diagram shows the layout of fixed points for a and unfamil- Competitive Dynamic Systems two-dimensional system (that is, weights for The competitive dynamic system strategy the Target and Obstacles constraints) as a func- iar tasks in models individual behaviors as the stable fixed tion of the perceptual parameters. In the fig- novel environ- points of a decision-making dynamic system. ure, the competitive advantage parameters are This system interacts with the signal level not varied from 0 to 1, assuming that the two com- ments. by invoking separate elementary behaviors but petitive interaction parameters remain fixed at by directly shaping perception-action strate- 0.5. Four qualitatively different regions (and, gies. The variables of the competitive dynamic thus, behaviors) are pictured. Activation of system determine the weighting of the task both constraints corresponds to the GoTo constraints in the behavioral dynamic system. behavior described earlier, but activation of This interface between the two levels allows the Obstacles constraint only corresponds to the decision-making system to activate and the Escape behavior. deactivate attractors and repellors in the per- Beginning in the front left corner of the ception-action system, synthesizing control parameter space, only the Obstacles constraint laws on the fly. Thus, qualitatively different contributes to the behavioral dynamics (the configurations of the weights give rise to dis- Escape behavior). Moving to the right, as Tar- tinct perception-action behaviors. In addition, get‘s advantage increases beyond 0.5, both Tar- distinct weight configurations, which arise as get and Obstacles constraints contribute to attractors in the competitive dynamic system, shape perception-action behavior (the GoTo are functionally equivalent to the FSM states of behavior). As we next decrease the advantage the discrete-event system. of Obstacles, moving to the back left region, Decisions are made through bifurcations in Obstacles is deactivated, but Target is activat-

FALL 1999 63 Articles

ed. If we once again decrease Target, moving and reasoning might be unnecessary because toward the back left region of the parameter many apparently intelligent behaviors can be space, we notice that Obstacles remains inac- modeled as perception-action systems situated tive, but Target remains active. This last region in the physical world. Unfortunately, we have of the state space is different from the others. come to view both points of view as somewhat Two behaviors are stable in this region. How- simplistic. Intelligent agents must be capable ever, the system can only occupy one of these of bringing to bear a rich variety of perception- states at any given time. In this case, the state action strategies but, at the same time, reason- of the system is determined by its recent histo- ing and solving problems to perform both ry, a phenomenon known as hysteresis. Finally, familiar and unfamiliar tasks in novel environ- the boundaries of the four regions are deter- ments. mined by the values of the competitive inter- In this regard, the study of intelligent phys- What are the action parameters. When competitive interac- ical agents and their behavior is of tremendous special tions change, the relative sizes of the different theoretical and practical significance in AI. Not stable regions change as well. Each of these dif- only are there a vast number of real-world requirements ferent regions corresponds to the execution of applications where autonomous agents can be of systems a qualitatively different perception-action useful, but models of intelligent physical behavior. agents can serve as valuable starting points for that must theories of intelligent biological systems. The interact with Comparing Approaches question that we ask is how to integrate mod- We tested the two systems described previous- els of perception-action behavior with models the physical ly to evaluate their relative performance in of problem-solving behavior. world and autonomous navigation tasks (Large, Chris- Although we do not yet have the answer, we also reason tensen, and Bajscy 1999). We vary environ- have two requirements for any solution: First, mental complexity by constructing environ- the description of the physical agent should and solve ments with different numbers of obstacles take place within a structured framework that problems? arranged in various configurations. We also supports both analysis and theory making. vary task complexity; for example, a single Thus, we are allowed to develop design It is this agent performs simple navigation, or a pair of methodologies for artificial agents as well as question that agents performs a cooperative task. In a range develop rational theories of biological agents. of tests, dynamic agents perform tasks faster Furthermore, any methodology that we pro- must be and more reliably than discrete-event agents. pose should be compositional, allowing man- addressed They are able to maintain higher mean veloci- ageable and flexible system design through ties, finding targets faster and with lower fail- decomposition of complex problems or behav- before we can ure rates than discrete-event agents. However, iors into subparts. Both of our systems meet claim a the discrete-event agents also have advantages. these requirements. Finally, it is necessary to theory of They obey task constraints more faithfully, carefully compare the assumptions brought to reluctant to relax constraints regardless of bear by different strategies as we learn to mod- intelligent environmental complexity. These differences el intelligent behavior in the real world. physical are the result of the model of decision making. What are the special requirements of sys- The symbol-level dynamic system changes tems that must interact with the physical agents and state less often than the discrete-event system, world and also reason and solve problems? It is before AI and especially in the face of a noisy sensor reading. this question that must be addressed before we Although our experiments are not yet conclu- can claim a theory of intelligent physical robotics can sive, by comparing modeling strategies in a agents and before AI and robotics can be be reunited. careful way, we are gaining important insights reunited. into the special requirements of systems that References must manipulate both signals and symbols at the same time and toward the same goal. Alami, R.; Chatila, R.; Fleury, S.; Ghallab, M.; and Ingrand, F. 1998. An Architecture for Autonomy. International Journal of Robotics Research 17(4): Conclusions 315–337. Arkin, R. C. 1998. Behavior-Based Robotics. Cam- Early in the history of AI, many researchers bridge, Mass.: MIT Press. came to believe that perception and action Brockett, R. W. 1993. Hybrid Models for Motion could be modeled by relatively simple trans- Control Systems. In Essays in Control: Perspectives in duction mechanisms, and therefore, abstract the Theory and Its Applications, eds. H. L. Trentelman reasoning and problem solving were the diffi- and J. C. Willems, 29–53. Boston: Birkhauser. cult issues worthy of study. More recently, it Clementini, E.; Di Felice, P.; and Hernandez, D. has been argued that complex representation 1997. Qualitative Representation of Positional Infor-

64 AI MAGAZINE Articles mation. Artificial Intelligence 95(2): 317–356. Ruzena Bajcsy obtained her first Cohn, A. G. 1995. The Challenge of Qualitative Spa- Ph.D. in EE from Slovak Technical tial Reasoning. Computing Surveys 27(3): 323–327. University (Czechoslovakia) in 1967; she was the first woman in Dickmanns, E. D. 1997. Vehicles Capable of Dynamic Slovakia ever to obtain a Ph.D. She Vision: A New Breed of Technical Beings? In Proceed- then went to Stanford University ings of the Fifteenth International Joint Conference and obtained her second Ph.D. in on Artificial Intelligence. Menlo Park, Calif.: Interna- 1972, studying AI under John tional Joint Conferences on Artificial Intelligence. McCarthy. Her dissertation topic Koenig, S., and Simmons, R. G. 1998. XAVIER: A Robot was machine perception, and she wrote one of the Navigation Architecture Based on Partially Observ- first programs enabling recognition of textured pat- able Markov Decision Process Models. In AI and terns. Mobile Robots, eds. D. Kortenkemp, R. P. Bonasso, She then joined the faculty at the University of and R. Murphy. Cambridge, Mass.: MIT Press. Pennsylvania, where she has continued her work on Kosecka, J. 1996. A Framework for Modeling and machine perception and computer vision, character- Verifying Visually Guided Agents: Design, Analysis, izing and solving problems involving segmentation, and Experiments, GRASP TR 402, Ph.D. dissertation, three-dimensional vision, and other sensory modal- School of Engineering and Applied Science, Univer- ities that function together with vision (for example, sity of Pennsylvania. touch). Bajcsy’s General Robotic Active Sensory Per- Large, E. W.; Christensen, H. I.; and Bajscy, R. 1999. ception (GRASP) Laboratory at the University of Scaling the Dynamic Approach to Path Planning and Pennsylvania is internationally recognized in the Control: Competition among Behavioral Con- field. straints. International Journal of Robotics Research She chaired her department from 1984 through 18(1): 37–58. 1986 and has served on numerous National Research Nagel, H. H., and Haag, M. 1998. Bias-Corrected Council and National Science Foundation (NSF) Optical Flow Estimation for Road Vehicle Tracking. advisory boards and committees. She was elected a In Proceedings of the International Conference on Com- fellow of the Institute of Electrical and Electronics puter Vision (ICCV ‘98), 1006–1011, New Delhi, Engineers in 1992 and the Association of Computing India: Narosa. Machinery in 1995; she is also a founding member Nerode, A., and Remmel, J. B. 1991. A Model for of the American Association for Artificial Intelli- Hybrid Systems. Paper presented at the Hybrid Sys- gence. In 1995, she was elected as a member of the tems Workshop, 17–19 May, Ithaca, New York. National Institute of Medicine, and she became a Ramadge, P. J., and Wonham, W. M. 1987. Supervi- member of the National Academy of Engineering in sory Control of a Class of Discrete Event Processes. 1997. She has served for three years on the CRA SIAM Journal on Control and Optimization 25(1): board. 206–230. Bajcsy is currently the assistant director of the NSF Russell, S. J., and Subramanian, D. 1995. Provably Directorate for Computer and Information Science Bounded-Optimal Agents. Journal of AI Research 2(1): and Engineering. Her e-mail address is bajcsy@cen- 575–609. tral.cis.upenn.edu. Sandewall, E. 1994. Features and Fluents: The Repre- sentation of Knowledge about Dynamical Systems. Oxford, U.K.: Oxford University Press. Schoner, G.; Dose, M.; and Engels, C. 1996. Dynam- Edward Large is an assistant professor at the Center ics of Behaviour: Theory and Applications for for Complex Systems at Florida Atlantic University. Autonomous Robot Architectures. Robotics and He received his Ph.D. from The Ohio State Universi- Autonomous Systems 16(4): 213–246. ty in 1994. He has held fellowships in cognitive sci- Shi, J., and Malik, J. 1998. Self-Inducing Relational ence and psychology at the University of Pennsylva- Distance and Its Application to Image Segmentation. nia and in AI at Toshiba’s Research and Development In Proceedings of the 1998 European Conference on Laboratory in Kawasaka, Japan. His research focuses Computer Vision, Volume 1, eds. H. Burkhardt and B. on the dynamics of human perception, action, and Neumann, 528–543. Lecture Notes in Computer Sci- cognition and the design of autonomous agents ence. Berlin: Springer Verlag. using dynamic principles. His areas of specialization Steinhage, A., and Schoner, G. 1998. Dynamic Sys- include music perception, auditory perception, and tems for the Behavioral Organization of robotics. His e-mail address is [email protected]. Autonomous Robot Navigation. In Sensor Fusion and edu. Decentralized Control in Robotic Systems: Proceedings of the International Society for Optical Engineering, eds. P. S. Schenker and G. T. McKee, 169–180. Bellingham, Wash.: International Society for Optical Engineer- ing. Tari, S.; Shah, J.; and Pien, H. 1997. Extraction of Shape Skeletons from Gray-Scale Images. Computer Vision Image Understanding (Special Issue on Biomed- ical Image Analysis) 66:133–146.

FALL 1999 65 Edited by Jeffrey Bradshaw

he chapters in this book examine the state of today’s agent technology and point the way toward the exciting developments of the next millennium. Contributors include Donald A. Norman, Nicholas Negroponte, TBrenda Laurel, Thomas Erickson, Ben Shneiderman, Thomas W. Malone, Pattie Maes, David C. Smith, Gene Ball, Guy A. Boy, Doug Riecken, Yoav Shoham, Tim Finin, Michael R. Genesereth, Craig A. Knoblock, Philip R. Cohen, Hector J. Levesque, and James E. White, among others.

Published by the AAAI Press / The MIT Press 500 pp., $42.00 ISBN 0-262-52234-9 Prices higher outside the U.S. and subject to change without notice. To order, call 800-356-0343 (US and Canada) or (617) 625-8569. Distributed by The MIT Press, 55 Hayward, Cambridge, MA 02142

66 AI MAGAZINE