Wszystkie pola: planning complexity - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Non-cooperative game approach to multi-robot planning
Autorzy:: Gałuszka, A.
Świerniak, A.
Powiązania:: https://bibliotekanauki.pl/articles/908521.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: zagadnienie projektowe
robot wielofunkcyjny
gra niekooperatywna
planowanie kompleksowe
planning problems
multi-robot environment
STRIPS language
non-cooperative games
planning complexity
Opis:: A multi-robot environment with a STRIPS representation is considered. Under some assumptions such problems can be modelled as a STRIPS language (for instance, a Block World environment) with one initial state and a disjunction of goal states. If the STRIPS planning problem is invertible, then it is possible to apply the machinery for planning in the presence of incomplete information to solve the inverted problem and then to find a solution to the original problem. In the paper a planning algorithm that solves the problem described above is proposed and its computational complexity is analyzed. To make the plan precise, non-cooperative strategies are used.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2005, 15, 3; 359-367
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Emotion learning: solving a shortest path problem in an arbitrary deterministic environment in linear time with an emotional agent
Autorzy:: Petruseva, S.
Powiązania:: https://bibliotekanauki.pl/articles/907900.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: czynnik emocjonalny
złożoność
programowanie
sieć neuronowa
emotional agent
complexity
consequence programming
CAA neural network
planning
Opis:: The paper presents an algorithm which solves the shortest path problem in an arbitrary deterministic environment with n states with an emotional agent in linear time. The algorithm originates from an algorithm which in exponential time solves the same problem, and the agent architecture used for solving the problem is an NN-CAA architecture (neural network crossbar adaptive array). By implementing emotion learning, the linear time algorithm is obtained and the agent architecture is modified. The complexity of the algorithm without operations for initiation in general does not depend on the number of states n, but only on the length of the shortest path. Depending on the position of the goal state, the complexity can be at most O(n). It can be concluded that the choice of the function which evaluates the emotional state of the agent plays a decisive role in solving the problem efficiently. That function should give as detailed information as possible about the consequences of the agent’s actions, starting even from the initial state. In this way the function implements properties of human emotions.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2008, 18, 3; 409-421
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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