Behavior control as a function of consciousness. II. Synthesis of a behavior plan

Osipov, G. S.; Panov, Aleksandr I.; Чудова, Н. В.

doi:10.1134/s106423071505010x

Cited by 23 publications

(2 citation statements)

References 7 publications

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“…On sign level behavior planning is realized in the situation space by P M A algorithm proposed in [51]. The situation is defined as the set of signs structured in the same way as the image components (see above), e. g. signs are split into groups which describe different parts of the situation.…”

Section: Behavior Planning Algorithmmentioning

confidence: 99%

Behavior and Path Planning for the Coalition of Cognitive Robots in Smart Relocation Tasks

Panov¹,

Yakovlev²

2016

Advances in Intelligent Systems and Computing

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Abstract. In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location. The latter is originally unreachable for some members of the coalition, but the common task still can be accomplished as the agents can assist each other (e.g. by modifying the environment). We call such tasks smart relocation tasks (as the can not be solved by pure path planning methods) and study spatial and behavior interaction of robots while solving them. We use cognitive approach and introduce semiotic knowledge representation -sign world model which underlines behavioral planning methodology. Planning is viewed as a recursive search process in the hierarchical state-space induced by sings with path planning signs reside on the lowest level. Reaching this level triggers path planning which is accomplished by state of the art grid-based planners focused on producing smooth paths (e.g. LIAN) and thus indirectly guarantying feasibility of that paths against agent's dynamic constraints.

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Section: Behavior Planning Algorithmmentioning

confidence: 99%

Behavior and Path Planning for the Coalition of Cognitive Robots in Smart Relocation Tasks

Panov¹,

Yakovlev²

2016

Advances in Intelligent Systems and Computing

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“…We use knowledge representation framework proposed in [16,17] for world model description. Within this framework the grounded symbol [7] is defined by the four component structure entitled a sign -name, image, significance and personal meaning.…”

Section: Introductionmentioning

confidence: 99%

Psychologically Inspired Planning Method for Smart Relocation Task

Panov

Yakovlev

2016

Procedia Computer Science

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Behavior planning is known to be one of the basic cognitive functions, which is essential for any cognitive architecture of any control system used in robotics. At the same time most of the widespread planning algorithms employed in those systems are developed using only approaches and models of Artificial Intelligence and don't take into account numerous results of cognitive experiments. As a result, there is a strong need for novel methods of behavior planning suitable for modern cognitive architectures aimed at robot control. One such method is presented in this work and is studied within a special class of navigation task called smart relocation task. The method is based on the hierarchical two-level model of abstraction and knowledge representation, e.g. symbolic and subsymbolic. On the symbolic level sign world model is used for knowledge representation and hierarchical planning algorithm, MAP, is utilized for planning. On the subsymbolic level the task of path planning is considered and solved as a graph search problem. Interaction between both planners is examined and inter-level interfaces and feedback loops are described. Preliminary experimental results are presented.

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