A. Saotti, K. Konolige, E. H. Ruspini, A multivalued-logic approach to integrating planning and control, Arti cial Intelligence Journal 76 (1-2) (1995) 481-526. 16  Home/Search   Document Details and Download   Summary   Related Articles   Check

....similar to the subsumption style architecture in [19] Each behavior contributes to the overall control decision according to its level of activation which in turn depends on its applicability in the current context. This fuzzy approach to robot behavior coordination was originally proposed in [20]. Tunstel et al. employ genetic programming to evolve supervisory fuzzy rules for the coordination of low level fuzzy behaviors. The evolved goal seeking behavior demonstrated modest generalization capability to previously unseen situations. In an earlier paper the author proposed a messy genetic ....

....in the design phase. Evolutionary algorithms provide an alternativefor automated learning and tuning of robotic behaviors[24] In the past fuzzy control systems have been successfully used to implemented robotic wall following, obstacle avoidance and navigation behaviors [1] 25] 22] [20]. In addition roboticists used fuzzy logic for behavior coordination in awaythatallows a smooth transition among di erent behaviors as an alternative to strictly prioritized arbitration scheme[20] 18] The arbitration policy is formulated by fuzzy context rules that de ne the degree to which a ....

[Article contains additional citation context not shown here]

A. Saotti, K. Konolige, and E.H. Ruspini, \A multi-valued logic approachtointegrating planning and control," Arti cial Intelligence,vol. 76, no. 1-2, pp. 481-526, 1995.

....continuously sample the low level configuration space. Other integrated architectures [2] 6] 9] 12] 14] utilize potential fields [11] in their reactive controllers to encode continuous responses, which are subject to local minima problems [13] In contrast, integrated architectures [24] [25] that employ discrete response encoding (i.e. finite, enumerated set of responses) encode high level motion commands (e.g. forward,left,right, etc. which may not be physically realizable due to negligence of kinematic constraints (e.g. non holonomy) Interpolation of these discrete ....

....a vector sum of action commands, each optimal to its respective behavior, to produce a combined output that may not guarantee the satisfaction of any active behaviors. Problems of local minima and no passage between narrowly spaced obstacles may arise [13] Orientation selection models [3] [25], 26] face similar problems due to a lack of distance perspective. This class of methods allow a robot with long range sensors to detect and avoid complex obstacles but the distant presence of a narrow doorway may be missed due to poor resolution at long range. It also cannot slow down while ....

[Article contains additional citation context not shown here]

A. Saffiotti, K. Konolige, and E. Ruspini. A multi-valued logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

....for fusion: Voting techniques interpret the output of each behavior as votes for or against possible actions and the action with the maximum weighted sum of votes is selected. CAMPOUT implements a DAMN style [8] voting algorithm based on BISMARC [9] Fuzzy command fusion mechanisms (see [10 11]) use fuzzy logic and inference to formalize the action selection processes. In addition, fuzzy approaches enable a new class of coordination mechanisms denoted context dependent blending, introduced to robotics by Saffiotti, Ruspini, and Konolige in [10] which allow for weighted combination of ....

..... Fuzzy command fusion mechanisms (see [10 11] use fuzzy logic and inference to formalize the action selection processes. In addition, fuzzy approaches enable a new class of coordination mechanisms denoted context dependent blending, introduced to robotics by Saffiotti, Ruspini, and Konolige in [10], which allow for weighted combination of behaviors. The implementation in CAMPOUT follows that described in [10] Multiple objective behavior fusion provides a formal approach to behavior coordination based on multiple objective decision theory [12] Action selection consists of selecting an ....

[Article contains additional citation context not shown here]

A. Saffiotti, K. Konolige, and E.~H. Ruspini, "A multivalued logic approach to integrating planning and control," Artificial Intelligence, vol. 76, pp. 481--526, March 1995.

....which typically signifies a task switch, behaviour fusion is used for integration of output from multiple behaviours into a single control signal for the platform. By far the most popular method has been the use of potential fields [5] In addition, methods such as voting [6] and fuzzy rules [7] have been exploited. A notorious problem in many of these systems is the lack of a solid theoretical foundation for weight selection of di#erent behaviours for integration. Especially for task switches, the existing solutions are rather ad hoc. An alternative to these methods is the dynamical ....

A. Sa#otti, K. Konolige, and E. Ruspini. A multi-valued logic approach to integrating planning and control. Artificial Intelligence, 76(1--2):481--526, 1995.

....map of the environment [3, 4] Moreover, additional landmarks are often necessary to compensate uncertainties arising from control errors, sensor noise, or inaccurate models of the environment. The strategy proposed uses a reactive and goal oriented navigation system based on fuzzy set theory [5, 6]. Through this approach, environmental uncertainties are compensated. The planner specifies the global driving direction by means of linguistic instructions, e.g. turn left at the next junction, while the navigator explores the local environment and selects the appropri ate local path [7, ....

A. Saffiotti, E. H. Ruspini, und K. Konolige, A Multivalued Logic Approach to Integrating Planning and Control. Menlo Paxk, CA 94025, USA: Artificial Intelligence Center, SRI International, Technical Note No. 533, 4/1994.

....network to continuously sample the low level configuration space. Other integrated architectures ( 2] 9] utilize potential fields [8] in their reactive controllers to encode continuous responses, which are subject to local minima problems [10] In contrast, integrated architectures ( 15] [16]) that employ discrete response encoding (i.e. finite, enumerated set of responses) encode high level motion commands (e.g. forward, left, right, etc. which may not be physically realizable due to negligence of kinematic constraints (e.g. non holonomy) Interpolation of these ....

....c b b planning world model points check local obstacles Fig. 1. A framework for the tight integration of planning, target reaching, and obstacle avoidance. Problems of local minima and no passage between narrowly spaced obstacles may arise [10] Orientation selection models ( 3] [16]) face similar problems as distance information is not considered. This class of methods allow a robot with long range sensors to detect and avoid complex obstacles but the distant presence of a narrow doorway may be missed due to poor resolution at long range. It also cannot slow down while ....

[Article contains additional citation context not shown here]

A. Saffiotti, K. Konolige, and E. Ruspini. A multi-valued logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

....is allowed to change its value, for all others it s value is read only. This mechanism adds multi robot cooperation capabilities into the DD framework, fitting perfectly in the global context of agent technology [Age] in which we are doing this work. DD differs from most other BBS, e.g. Ark98,SKR95,Ros97] by using dynamical system theory for the definition and analysis of behaviors. Furthermore this structure supports a practical interface for the integration of a deliberative control layer, see Sec. 5, 6. The implementation, evaluation and analysis of DD behaviors for different types of ....

A. Saffiotti, K. Konolige, and E.H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 1995.

....;o cope with uncertain, incomplete or approximate information when going about its ;asks as a transportation or cleaning robot in administrative buildings, factories or hospitals. Moreover, i; has ;o identify sudden changes ;ha; i; perceives in its surroundings, and react and maneuver in real time [1]. A control system u;ilising fuzzy rules has recently been developed in ;his laboratory, and has been demons;rated ;o be able ;o guide a real AMR through a he;work of corridors of varying widths, containing occasional random obstructions such as fire extinguishers [2] hobo; intelligence Award) ....

A. Saffiotti, E. H. Ruspini, and K. Konoligc, A Mul- tivalued Logic Approach to Integrating Planning and Control. Menlo Park, CA 94025, USA: Artificial Intelligence Center, SRI International, Technical Note No. 533, 4/1994.

....other approaches to navigate in large scale environments. Many of them (Xavier [7] for example) need more detailed models of the environment and sophisticated algorithms (e.g. Markov decision process models) to determine an appropriate control action for the robot at all times. An other approach [14] uses, as we do, the superposition of di erent local behaviours (motor schemas) However, discrete context changes lead to discrete changes in control. The same holds for a variety of systems using topological maps (Dervish [12] for example) In our system this information from the topological ....

A. Saotti, K. Konolige, and E. Ruspini. A multi-valued logic approach to integrating planning and control. Arti cial Intelligence, 76(1-2):481-526, 1995.

.... for robots, where the most common is the three layer architecture [12] This architecture usually consist of a controller for basic behaviors, a sequencer for selecting the current set of behaviors and a deliberator for long term planning[12] Examples of such systems are the Saphira architecture [15] and the robot Homer by Erann Gat and Greg Dorais [9] An overview of the three layer architectures can be found in [8] Our work address the issue of storing information. The type of information stored within a robot can roughly be be divided into three main categories; environmental models, ....

A. Saotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Articial Intelligence, 76(1/2):481 526, 1995.

....map [Thrun, 1998, Thrun et al. 1998] We refer the reader to Borestein s book [Borenstein et al. 1996] Chapter 8) for a review of the problems and advantages using metrical maps as well as a description of several systems using this spatial representation. 10.4. 2 Fuzzy control Konolige et al. [Konolige et al. 1995] propose an approach for integrating planning and control based on behavior schemas, which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as ....

....schemas, which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as predicates on these trajecto ries. The proposed methodology is summarized as follows [Konolige et al. 1995]: 189 Our approach to integrating planning and control has focussed on grounding the ingredients of planning in physical actions, using the tools of multivalued logics. We started from the definition of basic types of movements, or control schemas, and of the way they can be combined or ....

K. Konolige, A. Saffiotti, and E. Ruspini. A multivalued logic ap- proach to integrating planning and control. Artificial Intelligence, 76:481-526, 1995.

....which typically signi es a task switch, behaviour fusion is used for integration of output from multiple behaviours into a single control signal for the platform. By far the most popular method has been the use of potential elds [6] In addition, methods such as voting [11] and fuzzy rules [12] have been exploited. A notorious problem in many of these systems is the lack of a solid theoretical foundation for weight selection of di erent behaviours for integration. Especially for task switches, the existing solutions are rather ad hoc. An alternative to these methods is the dynamical ....

A. Saotti, K. Konolige, and E. Ruspini. A multivalued logic approach to integrating planning and control. Arti cial Intelligence, 76(1-2):481-526, 1995.

....signals directly into effector operation. Plans from the symbolic layer are translated into programs in terms of these low level actions, whose execution may be fiexibly modified to cope with unforseen events in the environment (often, this is demonstrated in obstacle avoidance in navigation) [11, 2] are examples pro ving that this architecture can work. There is a problem, though. The flow of control from the strategic planning layer down to the control layer can be handled, as shown by the research just mentioned. But the information flow up to the planning layer is often impoverished. ....

A. Saffiotti, K. Konolige, and E. Ruspini. A multiva- lued logic approach to integrating planning and control. Artif. Intell., 76:481-526, 1995.

....[Thrun, 1998, Thrun et al. 1998] We refer the reader to Borestein s book [Borenstein et al. 1996] Chapter 8) for a review of the problems and advantages using metrical maps as well as a description of several systems using this spatial representation. 10.4. 2 Fuzzy control Konolige et al. [Konolige et al. 1995] propose an approach for integrating planning and control based on behavior schemas, which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as ....

....schemas, which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as predicates on these trajectories. The proposed methodology is summarized as follows [Konolige et al. 1995] : 189 Our approach to integrating planning and control has focussed on grounding the ingredients of planning in physical actions, using the tools of multivalued logics. We started from the definition of basic types of movements, or control schemas, and of the way they can be combined or ....

K. Konolige, A. Saffiotti, and E. Ruspini. A multivalued logic approach to integrating planning and control. Artificial Intelligence, 76:481--526, 1995.

....other approaches to navigate in large scale environments. Many of them (Xavier [5] for example) need more detailed models of the environment and sophisticated algorithms (e.g. Markov decision process models) to determine an appropriate control action for the robot at all times. An other approach [9] uses, as we do, the superposition of di erent local behaviours (motor schemas) However, discrete context changes lead to discrete changes in control. In our system this information from the topological map can be merged with the continuous nature of the individual behaviours using the dynamical ....

A. Saotti, K. Konolige, and E. Ruspini. A multi-valued logic approach to integrating planning and control. Arti cial Intelligence, 76(1-2):481-526, 1995.

....An autonomous mobile robot (AMR) has to cope with uncertain, incomplete or approximate information. Moreover it G V G U V j U has to identify sudden perceptual situations and to manoeuvre in real time. Therefore an AMR has been often used as a testbed for fuzzy logic strategies [1][12][16] Our proposed hardware solution has several advantages for a successful implementation like: fast response time, real time behaviour; analog I O, direct control of the actuators (motors) reconfigurable rule base, flexibility. Our in house autonomous system MORIA (Fig. 12) was used ....

.A. Saffiotti, et al., A Multivalued Logic Approach to Integrating Planning and Control, Artificial Intelligence Center, SRI International, Technical Note No. 533, Menlo Park, CA 94025, USA, 4/1994.

....partial and approximate. This has an impact on both the naviagtion and the task planning of the autonomous system. Sensing is noisy, the dynamics of the environment can only be partially predicted and the hard and software tasks executed by the system (robot) are not completely reliable [16]. Classical path planning approaches have been criticised for not being able to cope adequately with this situation 3 . Traditional approaches to mobile robot navigation have dealt with these requirements by using computationally intensive planning algorithms and explicit pre determined world ....

A. Saffiotti, E. H. Ruspini, and K. Konolige, A Multivalued Logic Approach to Integrating Planning and Control. Menlo Park, CA 94025, USA: Artificial Intelligence Center, SRI International, Technical Note No. 533, 4/1994.

....is acknowledged for different time periods (i.e. 5 ms, 10 ms, etc. 4. Coordination Coordination in ART was based on the underlying EIEP described in the previous section. Hoewever since not all the robots were based on the ETHNOS platform (the two robots from Rome were based on Saphira [5]) a special communication library was developed to allow these two platforms to communicate transparently. Each robot of the ART team subscribed to the team club. One was the goalkeeper and the other three were players with interchangeable roles which were organized in a formation structure ....

A. Saffiotti, K. Konolidge, and E. H. Ruspini, A Multivalued-Logic Approach to Integrating Planning and Control, Artificial Intelligence, 76(1-2), 481-526, 1995

....similar to the subsumption style architecture in [19] Each behavior contributes to the overall control decision according to its level of activation which in turn depends on its applicability in the current context. This fuzzy approach to robot behavior coordination was originally proposed in [20]. Tunstel et al. employ genetic programming to evolve supervisory fuzzy rules for the coordination of low level fuzzy behaviors. The evolved goal seeking behavior demonstrated modest generalization capability to previously unseen situations. In an earlier paper the author proposed a messy genetic ....

....in the design phase. Evolutionary algorithms provide an alternative for automated learning and tuning of robotic behaviors[24] In the past fuzzy control systems have been successfully used to implemented robotic wall following, obstacle avoidance and navigation behaviors [1] 25] 22] [20]. In addition roboticists used fuzzy logic for behavior coordination in a way that allows a smooth transition among di erent behaviors as an alternative to strictly prioritized arbitration scheme[20] 18] The arbitration policy is formulated by fuzzy context rules that de ne the degree to which ....

[Article contains additional citation context not shown here]

A. Saotti, K. Konolige, and E.H. Ruspini, \A multi-valued logic approach to integrating planning and control," Articial Intelligence, vol. 76, no. 1-2, pp. 481-526, 1995.

.... (p q; s) p; s) Omega (q; s) and Omega is any continuous triangular norm (e.g. min(p; q) pq) L is a propositional language over P and the logical connectives and , where (p q; s) p; s) Phi (q; s) and Phi is any continuous triangular conorm (e.g. max(p; q) x y Gamma xy) Saffiotti et al. 1995 ] REASM behavior networks B are described by a tuple (G; M; Pi) where ffl G denotes the set of goals characterized as tuples (GCon, RCon) with GCon 2 L the goal condition, i.e. the situation in which the goal is satisfied, the importance of the goal 2 [0: 1] RCon 2 L the ....

Saffiotti, A., Konolige, K. and Ruspini, E. (1995). A Multivalued Logic Approach to Integrating Planning and Control. In Artificial Intelligence, Vol 76, No 1-2, pages 481-526.

....specification of control and of goal achievement by execution of a plan (i.e. a series of actions) are quite separated, therefore not much research has been devoted to this problem yet. Some interesting work has recently been done in this direction by Saffiotti, Konolige and Ruspini [SKR93, SRK93] We will return to this problem later in this paper. 3 Three layered software architecture Our approach to autonomous real time systems builds on the three layered software architecture (shown in Figure 1) developed in our group [HNS89, MS91, MNT OS92] since 1986. The three layers are ....

Alessandro Saffiotti, Kurt Konolige, and Enrique H. Ruspini. A multivalued logic approach to integrating planning and control. Technical Note 533, Artificial Intelligence Center, SRI International, Menlo Park, CA, U.S.A., 1993.

....language providing goals for an off line planner which generates REX programs for execution. Multivalued logic is used to control the robot Flakey, where the control program takes the form of a fuzzy logic rule based system. Multivalued logic also has been used to analyze how behaviors combine[44]. Given that each behavior has an explicit applica Fig. 31. The mission executing in the MissionLab simulator. The robots started in the bottom left corner moving up in line formation, then moved right in column formation, and are now moving to the right in a wedge formation. bility context, ....

A. Saffiotti, Kurt Konolige, and E Ruspini. A multivalued logic approach to integrating planning and control. Technical Report 533, SRI Artificial Intelligence Center, Menlo Park, California, 1993.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini, "A multivalued-logic approach to integrating planning and control," Artificial Intelligence, vol. 76, no. 1-2, pp. 481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini, "A multivalued-logic approach to integrating planning and control," Artificial Intelligence, vol. 76, no. 1-2, pp. 481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995. Online at http://www.aass.oru.se/asaffio/.

No context found.

A. Saffiotti and K. Konolige and E.H. Ruspini, A multivalued-logic approach to integrating planning and control. Artificial Intelligence 76(12) :481-526, 1995.

No context found.

Saffiotti, A.; Konolige, K.; and Ruspini, E. 1995. A multivalued-logic approach to integrating planning and control. Artificial Intelligence 76(1--2):481--526.

No context found.

A. Safotti, K. Konolige, and E.H. Ruspini. A multivalued-logic approach to integrating planning and control. Articial Intelligence, 76(1-- 2):481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E.H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1--2):481--526, 1995.

No context found.

A. Safotti, K. Konolige, and E.H. Ruspini. A multivalued-logic approach to integrating planning and control. Articial Intelligence, 76(1-- 2):481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

Saffiotti, A., Konolige, K., and Ruspini, E.H. (1995) A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76, 1-2, 481--526. Online at http://www.aass.oru.se/~asaffio/

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481-- 526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

Saffiotti, A., Konolige, K., and Ruspini, E.H. A multivalued-logic approach to integrating planning and control. Artificial Intelligence 76(1-2):481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

....anchoring is typically solved on a system bysystem basis, and the solution is hidden in the code. The autonomous robotics and machine vision literature contains a few examples in which the need and the role of anchoring, under different names, has been explicitly identified, e.g. 6] 5] [7], 8] However, all the works above describe specific implementations and do not attempt a study Ingelligent Agent Physical World Sensori motoric system symbols table1 room3 Anchoring sensor data door21 observe denote mycup Symbolic reasoning system Fig. 1. The anchoring process of ....

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivaluedlogic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

....cars and is able to re acquire a car after a short disappearance, for instance due to a passage under a bridge. In the ground robot, we have implemented an anchoring module whose task is to bring a micro model of the world inside the controller, and to register this model against perceptual data [25, 16]. These solutions have given us the first insights about the general principles that underlie anchoring; these principles will in turn be used to develop more general solutions which will be tested on both platforms. Our initial motivation for studying the anchoring problem was the need to define ....

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivaluedlogic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

....information about objects and places relevant for the task. It can obtain additional information about objects from the anchoring module. The lower layer includes a navigation and a vision module. The navigation module is a simplified version of the fuzzy behavior based controller defined in [Saffiotti et al. 1995] , which executes the actions sent by the plan executor. An example of an action is (gonear A) The vision module contains vision routines for recognizing objects and for calculating properties such as color and size. The anchoring module provides the connection between the symbols used by the ....

....planning systems only consider specific, previously known objects. 6 Discussion The autonomous robotics and machine vision literature contains a few examples in which the need and the role of anchoring, under different names, has been explicitly identified, e.g. Hexmoor et al. 1993] [Saffiotti et al. 1995] , Jung and Zelinsky, 2000] Chella et al. 1998] Bajcsy and Kosecka, 1994] Satoh et al. 1997] Horswill, 1997] Wasson et al. 1999] However, all the works above describe specific implementations and do not attempt a study of the general concept of anchoring. To our knowledge, ....

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

A. Saotti, K. Konolige, E. H. Ruspini, A multivalued-logic approach to integrating planning and control, Arti cial Intelligence Journal 76 (1-2) (1995) 481-526. 16

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

A. Sa#otti, K. Konolige, and E.H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

A. Sa#otti, K. Konolige, and E.H. Ruspini, "A multi-valued logic approach to integrating planning and control," Artificial Intelligence, vol. 76, no. 1-2, pp. 481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. Ruspini, "A multivalued-logic approach to integrating planning and control," Artificial Intelligence, vol. 76, no. 1-2, pp. 481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planningand control. Artificial Intelligence, 76(1-2):481--526, 1995.

No context found.

A. Saotti, K. Konolige, and E.H. Ruspini. A multi-valued logic approach to integrating planning and control. Arti cial Intelligence, 76(1-2):418-526, 1995.

No context found.

A. Sa#otti, K. Konolige, and E.H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1-- 2):481--526, 1995.

No context found.

A. Saffiotti, K. Konolige, and E. H. Ruspini. A Multivalued Logic Approach to Integrating Planning and Control. Artificial Intelligence, 76(1--2):481--526, 1995.

No context found.

Sa#otti A, Konolige K, Ruspini EH. A multivalued logic approach to integrating planning and control. Arti#cial Intelligence 1993;12:481#526.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC