Madagascar: Scalable Planning with SAT
... Kautz and Selman (1996) proposed testing the satisfiability of Φt for different values of t = 0, 1, 2, . . . sequentially, until a satisfiable formula is found. This strategy is asymptotically optimal if the t parameter corresponds to the plan quality measure to be minimized, as it would with sequen ...
... Kautz and Selman (1996) proposed testing the satisfiability of Φt for different values of t = 0, 1, 2, . . . sequentially, until a satisfiable formula is found. This strategy is asymptotically optimal if the t parameter corresponds to the plan quality measure to be minimized, as it would with sequen ...
The play within the play
... Tower of Hanoi The legend: In an Indian temple there is a large room with three posts in it surrounded by 64 golden disks. Brahmin priests, acting out the command of an ancient prophecy, have been moving these disks. According to the legend, when the last move of the puzzle will be completed, the w ...
... Tower of Hanoi The legend: In an Indian temple there is a large room with three posts in it surrounded by 64 golden disks. Brahmin priests, acting out the command of an ancient prophecy, have been moving these disks. According to the legend, when the last move of the puzzle will be completed, the w ...
A Survey on Application of Bio-Inspired Algorithms
... motivates the researchers to search for efficient methods. Divide and conquer techniques are the one way to solve large and complex problems which has been a practice in research since long time. Swarms have relatively simple behaviours individually, but with amazing capability of co-ordination and ...
... motivates the researchers to search for efficient methods. Divide and conquer techniques are the one way to solve large and complex problems which has been a practice in research since long time. Swarms have relatively simple behaviours individually, but with amazing capability of co-ordination and ...
artificial intelligence - MET Engineering College
... 1.1.4 The state of art What can A1 do today? Autonomous planning and scheduling: A hundred million miles from Earth, NASA's Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonsson et al., 2000). Remote Agent generat ...
... 1.1.4 The state of art What can A1 do today? Autonomous planning and scheduling: A hundred million miles from Earth, NASA's Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonsson et al., 2000). Remote Agent generat ...
ANN Models Optimized using Swarm Intelligence Algorithms
... – it may get stuck at a local optimum [28] and it may take a very long time to converge [47]. ...
... – it may get stuck at a local optimum [28] and it may take a very long time to converge [47]. ...
5 - People Server at UNCW
... This membrane structure can be nested so that parents can pass directly compare our results with other published results, we good solutions through a membrane to their parent membrane. have focused on a particular CBA problem which is the most This grandparent membrane may perform similar reactions. ...
... This membrane structure can be nested so that parents can pass directly compare our results with other published results, we good solutions through a membrane to their parent membrane. have focused on a particular CBA problem which is the most This grandparent membrane may perform similar reactions. ...
metaheuristic approaches for the berth allocation problem
... two phases alternate until stopping condition is fulfilled. GRASP is introduced by Feo and Resende [21]. In the first phase, greedy algorithm is used to make the decision about the next step in solution construction. Algorithm always chooses actions that look best at the moment, resulting in solutio ...
... two phases alternate until stopping condition is fulfilled. GRASP is introduced by Feo and Resende [21]. In the first phase, greedy algorithm is used to make the decision about the next step in solution construction. Algorithm always chooses actions that look best at the moment, resulting in solutio ...
Search --- Uninformed
... 2) Non-observable --- sensorless problem – Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions certain). 3) Nondeterministic and/or partially observable: contingency problem – Actions uncertain, percepts provide ne ...
... 2) Non-observable --- sensorless problem – Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions certain). 3) Nondeterministic and/or partially observable: contingency problem – Actions uncertain, percepts provide ne ...
Automated Agent Decomposition for Classical Planning
... bumping into each other. A particular instance of this domain is shown in Figure 1. There are three robots (a, b, c) which must all report to the starred location. Robots can move to any orthogonal adjacent empty grid square and report at the intended destination. For example, if robot a ia at locat ...
... bumping into each other. A particular instance of this domain is shown in Figure 1. There are three robots (a, b, c) which must all report to the starred location. Robots can move to any orthogonal adjacent empty grid square and report at the intended destination. For example, if robot a ia at locat ...
CS 4700: Foundations of Artificial Intelligence Bart Selman Problem
... 1) Deterministic, fully observable Agent knows exactly which state it will be in; solution is a sequence of actions. 2) Non-observable --- sensorless problem – Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions c ...
... 1) Deterministic, fully observable Agent knows exactly which state it will be in; solution is a sequence of actions. 2) Non-observable --- sensorless problem – Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions c ...
Selforganizology: A more detailed description
... approximation, estimate the variance and test statistical significance, and compute the expectation of function of random variables (Manly, 1997; Zhang and Shoenly, 1999a, b; Zhang and Schoenly, 2001; Ferrarini, 2011; Zhang, 2010, 2012; Zhang, 2011a, b, c; Zhang and Zheng, 2012). 2.2 Heuristic metho ...
... approximation, estimate the variance and test statistical significance, and compute the expectation of function of random variables (Manly, 1997; Zhang and Shoenly, 1999a, b; Zhang and Schoenly, 2001; Ferrarini, 2011; Zhang, 2010, 2012; Zhang, 2011a, b, c; Zhang and Zheng, 2012). 2.2 Heuristic metho ...
Proteus: Visual Analogy in Problem Solving
... major subtasks of analogy, but also uses a uniform knowledge representation for all the subtasks. Also as illustrated by ANALOGY and Letter Spirit, visual analogy refers to analogy based only on the appearance of a situation. e.g., the shape of the letter f and the spatial relationship between its c ...
... major subtasks of analogy, but also uses a uniform knowledge representation for all the subtasks. Also as illustrated by ANALOGY and Letter Spirit, visual analogy refers to analogy based only on the appearance of a situation. e.g., the shape of the letter f and the spatial relationship between its c ...
Problem Solving by Search - Cornell Computer Science
... People can find solns. But not necessarily minimum length. See solve it! (Gives strategy.) Korf, R., and Schultze, P. 2005. Large-scale parallel breadth-first search. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI-05). See Fifteen Puzzle Optimal Solver. With effectiv ...
... People can find solns. But not necessarily minimum length. See solve it! (Gives strategy.) Korf, R., and Schultze, P. 2005. Large-scale parallel breadth-first search. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI-05). See Fifteen Puzzle Optimal Solver. With effectiv ...
Evolving Real-time Heuristic Search Algorithms
... route, regardless of how distant the goal is. Another application of real-time heuristic search is distributed search such as routing in ad hoc sensor networks (Bulitko and Lee, 2006). Starting with LRTA* (Korf, 1990) real-time heuristic search agents interleave three processes: local planning, heur ...
... route, regardless of how distant the goal is. Another application of real-time heuristic search is distributed search such as routing in ad hoc sensor networks (Bulitko and Lee, 2006). Starting with LRTA* (Korf, 1990) real-time heuristic search agents interleave three processes: local planning, heur ...
Pathfinding Algorithms in Multi
... Figure 2.1: The nodes searched using Dijkstra’s Algorithm Patel (2010). Modern pathfinding methods used in games, are based on the Dijkstra’s algorithm, the most popular being the A* algorithm. A* algorithm is an improvement on Dijkstra as it uses heuristics to ensure better performance of the pathf ...
... Figure 2.1: The nodes searched using Dijkstra’s Algorithm Patel (2010). Modern pathfinding methods used in games, are based on the Dijkstra’s algorithm, the most popular being the A* algorithm. A* algorithm is an improvement on Dijkstra as it uses heuristics to ensure better performance of the pathf ...
Decentralized POMDPs
... models in the previous chapters do not suffice. An example of such a task is load balancing among queues (Cogill et al, 2004). Each agent represents a processing unit with a queue that has to decide whether to accept new jobs or pass them to another queue, based only on the local observations of its ...
... models in the previous chapters do not suffice. An example of such a task is load balancing among queues (Cogill et al, 2004). Each agent represents a processing unit with a queue that has to decide whether to accept new jobs or pass them to another queue, based only on the local observations of its ...
Improving the Efficiency of Dynamic Programming on Tree
... These models can be obtained in an off-line trainingphase by running several instances with different tree decompositions and by storing the runtime and the feature values which are then processed by machine learning algorithms. For our purposes, machine learning techniques need to predict a good ra ...
... These models can be obtained in an off-line trainingphase by running several instances with different tree decompositions and by storing the runtime and the feature values which are then processed by machine learning algorithms. For our purposes, machine learning techniques need to predict a good ra ...
Nash Social Welfare in Multiagent Resource Allocation
... agents negotiate a sequence of (typically small) local deals, one deal at a time. This approach has been studied in detail with utilitarian social welfare as the criterion of choice [19, 8, 7], and to a lesser extent also for various fairness criteria [8, 4]. Mirroring known results for other welfar ...
... agents negotiate a sequence of (typically small) local deals, one deal at a time. This approach has been studied in detail with utilitarian social welfare as the criterion of choice [19, 8, 7], and to a lesser extent also for various fairness criteria [8, 4]. Mirroring known results for other welfar ...
Problem Solving and Search (part 1)
... 2) Non-observable --- sensorless problem – Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions certain). 3) Nondeterministic and/or partially observable: contingency problem – Actions uncertain, percepts provide ne ...
... 2) Non-observable --- sensorless problem – Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions certain). 3) Nondeterministic and/or partially observable: contingency problem – Actions uncertain, percepts provide ne ...
HTN Planning Approach Using Fully Instantiated
... containing only primitive tasks achievable by actions and whose all associated constraints are verified. HTN planners can be divided into two categories [5]. This division is based on the nature of the research space used by these algorithms: Plan space based and state space algorithms. In the first ...
... containing only primitive tasks achievable by actions and whose all associated constraints are verified. HTN planners can be divided into two categories [5]. This division is based on the nature of the research space used by these algorithms: Plan space based and state space algorithms. In the first ...
Contrasting Effects of Reward Expectation on Sensory and Motor
... the pre-cue ‘control’ period (the 1 s duration before the cue onset) to examine whether the neuron showed significant task-related activities. If the mean discharge rate in a given period was significantly different from that in the control period (Mann--Whitney U-test, P < 0.05), the neuron was consi ...
... the pre-cue ‘control’ period (the 1 s duration before the cue onset) to examine whether the neuron showed significant task-related activities. If the mean discharge rate in a given period was significantly different from that in the control period (Mann--Whitney U-test, P < 0.05), the neuron was consi ...
to the PDF file
... The introduced problem is one that has been studied from ancient to modern times. We have shown how to find both of the solutions or the quickest one of them. At the same time the method gives us opportunity to find the solution when we have to deal with decanting jugs with high volume. Moreover, it ...
... The introduced problem is one that has been studied from ancient to modern times. We have shown how to find both of the solutions or the quickest one of them. At the same time the method gives us opportunity to find the solution when we have to deal with decanting jugs with high volume. Moreover, it ...
The complexity of planning - Dartmouth Computer Science
... be computationally much simpler, since in this case rewards cannot cancel each other out. We abstract the policy existence problems as follows: “Is there a policy with expected reward > 0?” We have chosen these decision problems because they can be used, along with binary search, to calculate the ex ...
... be computationally much simpler, since in this case rewards cannot cancel each other out. We abstract the policy existence problems as follows: “Is there a policy with expected reward > 0?” We have chosen these decision problems because they can be used, along with binary search, to calculate the ex ...
Full Text - Cerebral Cortex
... characteristic activity changes were observed in the ‘Visible food reward’ task (not shown), where actual food or an empty tray was presented as the cue, the differential activity observed in this neuron is not considered to be related to the difference in the color (red versus green) of the cue ind ...
... characteristic activity changes were observed in the ‘Visible food reward’ task (not shown), where actual food or an empty tray was presented as the cue, the differential activity observed in this neuron is not considered to be related to the difference in the color (red versus green) of the cue ind ...
Document
... SMA* expands the best leaf until memory is full and it drops the oldest worst leaf node and expands the newest best leaf node. 13. What is metalevel state space? Each state in a metalevel state space captures the internal state of a program that is searching in an object level state space. 14. What ...
... SMA* expands the best leaf until memory is full and it drops the oldest worst leaf node and expands the newest best leaf node. 13. What is metalevel state space? Each state in a metalevel state space captures the internal state of a program that is searching in an object level state space. 14. What ...
Multi-armed bandit
In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of slot machines (sometimes known as ""one-armed bandits"") has to decide which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in ""some aspects of the sequential design of experiments"".A theorem, the Gittins index published first by John C. Gittins gives an optimal policy in the Markov setting for maximizing the expected discounted reward.In practice, multi-armed bandits have been used to model the problem of managing research projects in a large organization, like a science foundation or a pharmaceutical company. Given a fixed budget, the problem is to allocate resources among the competing projects, whose properties are only partially known at the time of allocation, but which may become better understood as time passes.In early versions of the multi-armed bandit problem, the gambler has no initial knowledge about the machines. The crucial tradeoff the gambler faces at each trial is between ""exploitation"" of the machine that has the highest expected payoff and ""exploration"" to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in reinforcement learning.