Is it possible for the organizers of a sports tournament to influence the identity of the final w... more Is it possible for the organizers of a sports tournament to influence the identity of the final winner by manipulating the initial seeding of the tournament? Is it possible to ensure a specific good (i.e. king) player will win at least a certain number of rounds in the tournament? This paper investigates these questions both by means of a theoretical method and a practical approach. The theoretical method focuses on the attempt to identify sufficient conditions to ensure a king player will win at least a pre–defined number of rounds in the tournament. It seems that the tournament must adhere to very strict conditions to ensure the outcome, suggesting that this is a hard problem. The practical approach, on the other hand, uses the Monte Carlo method to demonstrate that these problems are solvable in realistic computational time. A comparison of the results lead to the realization that players with equivalent representation might relax the actual complexity of the problem, and enable manipulation of tournaments that can be controlled in reality.
This article focuses on the question of whether a certain candidate’s (player’s) chance to advanc... more This article focuses on the question of whether a certain candidate’s (player’s) chance to advance further in a tennis tournament can be increased when the ordering of the tournament can be controlled (manipulated by the organizers) according to his own preferences. Is it possible to increase the number of ranking points a player will receive? And most importantly, can it be done in reasonable computational time? The answers to these questions differ for different settings. e.g., the information available on the outcome of each game, the significance of the number of points gained or of the number of games won. We analyzed five different variations of these tournament questions. First the complexity hardness of trying to control the tournaments is shown. Then, the tools of parametrized complexity are used to investigate the source of the problems’ hardness. Specifically, we check whether this hardness holds when the size of the problem is bounded. The findings of this analysis show that it is possible under certain circumstances to control the tournament in favor of a specific candidate in order to help him advance further in the tournament.
In teamwork when a user and an agent are working together on a joint task it may be important to ... more In teamwork when a user and an agent are working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication be-tween agents and users can constitute costly user interruptions. One of the most important issue concerning the initiation of information shar-ing in teamwork is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been investigated in prior works, all of those works assumed either a large amount of information existed about each user, or only a small num-ber of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods together with classification al-gorithms tools in order to create a fast learning algorithm. This algorithm exploits the similarities between users in order to learn from known users to new but similar users and therefore demands less information o...
In teamwork when a user and an agent are working together on a joint task it may be important to ... more In teamwork when a user and an agent are working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication be-tween agents and users can constitute costly user interruptions. One of the most important issue concerning the initiation of information shar-ing in teamwork is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been investigated in prior works, all of those works assumed either a large amount of information existed about each user, or only a small num-ber of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods together with classification al-gorithms tools in order to create a fast learning algorithm. This algorithm exploits the similarities between users in order to learn from known users to new but similar users and therefore demands less information o...
Coalition formation is a key topic in multi–agent systems (mas). Coalitions enable agents to achi... more Coalition formation is a key topic in multi–agent systems (mas). Coalitions enable agents to achieve goals that they may not have been able to achieve independently, and encourages resource sharing among agents with different goals. A range of previous studies have found that problems in coalitional games tend to be computationally complex. However, such hardness results consider the entire input as one, ignoring any structural information on the instances. In the case of coalition formation problems, this bundles together several distinct elements of the input, e.g. the agent set, the goal set, the resources, etc. In this paper we reexamine the complexity of coalition formation problems in the coalition resources game model, as a function of their distinct input elements, using the theory of parameterized
Coalitions and cooperation are key topics in multi–agent systems (mas). They enable agents to ach... more Coalitions and cooperation are key topics in multi–agent systems (mas). They enable agents to achieve goals that they may not have been able to achieve independently. A range of previous studies have found that many problems in coalitional games tend to be computationally intractable- that is, the computational complexity grows rapidly as a function of the number of participating agents. However, these hardness results generally require that each agent is of a different type. Here, we observe that in many mas settings, while the number of agents may grow, the number of different types of agents remains small. We formally define the notion of agent types in cooperative games. We then re-examine the computational complexity of the different coalition formation problems when assuming that the number of agent types is fixed. We show that most of the previously hard problems become polynomial when the number of agent types is fixed. We consider multiple different game formulations and re...
In collaborative systems involving a user and an agent working together on a joint task it may be... more In collaborative systems involving a user and an agent working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can create costly user interruptions. One of the most important issue concerning the initiation of information sharing in collaborative systems is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been previously investigated, these works assumed either a large amount of information was available about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods with classi cation algorithms tools to create a fast learning algorithm, MICU. MICU exploits the similarities between users in order to learn from known users to new but similar users and therefore requires less information on each u...
A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can ... more A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can be used for modeling the static structure of a software system. Learning from errors is a teaching approach based on the assumption that errors can promote learning. We applied a constructive approach of using errors in designing a UML class diagram in order to (a) categorize the students’ errors when they design a class diagram from a text scenario that describes a specific organization and (b) determine whether the learning-from-errors approach enables students to produce more accurate and correct diagrams. The research was conducted with college students (N = 45) studying for their bachelor’s degree in engineering. The approach is presented, and the learning-fromerrors activity is illustrated. We present the students’ errors in designing the class diagram before and after the activity, together with the students’ opinions about applying the new approach in their course. Twenty errors ...
Interruptions can have a significant impact on users working to complete a task. When people are ... more Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often re-quire long training periods that make them unsuitable for on-line collaborative environments where new users frequently participate. In this paper, we present a novel synthesis between Collab-orative Filtering methods and machine learning classification algorithms to create a fast learning algorithm, CRISP. CRISP exploits the similarities between users in order to apply data from known users to new users, therefore requiring less in-formation on each person. Results from user studies indicate the algorithm significantly improves users ’ performances in completing the...
In teamwork when a user and an agent are working together on a joint task it may be important to ... more In teamwork when a user and an agent are working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can constitute costly user interruptions. One of the most important issue concerning the initiation of information sharing in teamwork is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been investigated in prior works, all of those works assumed either a large amount of information existed about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods together with classification algorithms tools in order to create a fast learning algorithm. This algorithm exploits the similarities between users in order to learn from known users to new but similar users and therefore demands less information on ea...
In collaborative systems involving a user and an agent working together on a joint task it may be... more In collaborative systems involving a user and an agent working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can create costly user interruptions. One of the most important issue concerning the initiation of information sharing in collaborative systems is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been previously investigated, these works assumed either a large amount of information was available about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods with classification algorithms tools to create a fast learning algorithm, MICU. MICU exploits the similarities between users in order to learn from known users to new but similar users and therefore requires less information on each user in compare to other methods. Experimental results indicate the algorithm significantly improves system performance even with a small amount of data on each user.
Coalition formation is a key topic in multi-agent systems (mas). Coalitions enable agents to achi... more Coalition formation is a key topic in multi-agent systems (mas). Coalitions enable agents to achieve goals that they may not have been able to achieve independently, and en- courages resource sharing among agents with different goals. A range of previous studies have found that problems in coalitional games tend to be computationally complex. However, such hardness results consider the entire input as one, ignoring any structural information on the instances. In the case of coalition formation problems, this bundles to- gether several distinct elements of the input, e.g. the agent set, the goal set, the resources, etc. In this paper we re- examine the complexity of coalition formation problems in the coalition resources game model, as a function of their distinct input elements, using the theory of parameterized complexity. The analysis shows that not all parts of the in- put are created equal, and that many instances of the prob- lem are actually tractable. We show that the problems are FPT in the number of goals, implying that if the number of goals is bounded then an efficient algorithm is available. Similarly, the problems are FPT in the combination of the number of agents and resources, again implying that if these parameters are bounded, then an efficient algorithm is avail- able. On the other hand, the problems are para- NP hard in the number of resources, implying that even if we bound the number of resources the problems (probably) remain hard. Additionally, we show that most problems are W(1)-hard in the size of the coalition of interest, indicating that there is (probably) no algorithm polynomial in all but the coalition size. The exact definitions of the parameterized complexity notions FPT, Para- NP and W(1) are provided herein.
Is it possible for the organizers of a sports tournament to influence the identity of the final w... more Is it possible for the organizers of a sports tournament to influence the identity of the final winner by manipulating the initial seeding of the tournament? Is it possible to ensure a specific good (i.e. king) player will win at least a certain number of rounds in the tournament? This paper investigates these questions both by means of a theoretical method and a practical approach. The theoretical method focuses on the attempt to identify sufficient conditions to ensure a king player will win at least a pre–defined number of rounds in the tournament. It seems that the tournament must adhere to very strict conditions to ensure the outcome, suggesting that this is a hard problem. The practical approach, on the other hand, uses the Monte Carlo method to demonstrate that these problems are solvable in realistic computational time. A comparison of the results lead to the realization that players with equivalent representation might relax the actual complexity of the problem, and enable manipulation of tournaments that can be controlled in reality.
This article focuses on the question of whether a certain candidate’s (player’s) chance to advanc... more This article focuses on the question of whether a certain candidate’s (player’s) chance to advance further in a tennis tournament can be increased when the ordering of the tournament can be controlled (manipulated by the organizers) according to his own preferences. Is it possible to increase the number of ranking points a player will receive? And most importantly, can it be done in reasonable computational time? The answers to these questions differ for different settings. e.g., the information available on the outcome of each game, the significance of the number of points gained or of the number of games won. We analyzed five different variations of these tournament questions. First the complexity hardness of trying to control the tournaments is shown. Then, the tools of parametrized complexity are used to investigate the source of the problems’ hardness. Specifically, we check whether this hardness holds when the size of the problem is bounded. The findings of this analysis show that it is possible under certain circumstances to control the tournament in favor of a specific candidate in order to help him advance further in the tournament.
In teamwork when a user and an agent are working together on a joint task it may be important to ... more In teamwork when a user and an agent are working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication be-tween agents and users can constitute costly user interruptions. One of the most important issue concerning the initiation of information shar-ing in teamwork is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been investigated in prior works, all of those works assumed either a large amount of information existed about each user, or only a small num-ber of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods together with classification al-gorithms tools in order to create a fast learning algorithm. This algorithm exploits the similarities between users in order to learn from known users to new but similar users and therefore demands less information o...
In teamwork when a user and an agent are working together on a joint task it may be important to ... more In teamwork when a user and an agent are working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication be-tween agents and users can constitute costly user interruptions. One of the most important issue concerning the initiation of information shar-ing in teamwork is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been investigated in prior works, all of those works assumed either a large amount of information existed about each user, or only a small num-ber of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods together with classification al-gorithms tools in order to create a fast learning algorithm. This algorithm exploits the similarities between users in order to learn from known users to new but similar users and therefore demands less information o...
Coalition formation is a key topic in multi–agent systems (mas). Coalitions enable agents to achi... more Coalition formation is a key topic in multi–agent systems (mas). Coalitions enable agents to achieve goals that they may not have been able to achieve independently, and encourages resource sharing among agents with different goals. A range of previous studies have found that problems in coalitional games tend to be computationally complex. However, such hardness results consider the entire input as one, ignoring any structural information on the instances. In the case of coalition formation problems, this bundles together several distinct elements of the input, e.g. the agent set, the goal set, the resources, etc. In this paper we reexamine the complexity of coalition formation problems in the coalition resources game model, as a function of their distinct input elements, using the theory of parameterized
Coalitions and cooperation are key topics in multi–agent systems (mas). They enable agents to ach... more Coalitions and cooperation are key topics in multi–agent systems (mas). They enable agents to achieve goals that they may not have been able to achieve independently. A range of previous studies have found that many problems in coalitional games tend to be computationally intractable- that is, the computational complexity grows rapidly as a function of the number of participating agents. However, these hardness results generally require that each agent is of a different type. Here, we observe that in many mas settings, while the number of agents may grow, the number of different types of agents remains small. We formally define the notion of agent types in cooperative games. We then re-examine the computational complexity of the different coalition formation problems when assuming that the number of agent types is fixed. We show that most of the previously hard problems become polynomial when the number of agent types is fixed. We consider multiple different game formulations and re...
In collaborative systems involving a user and an agent working together on a joint task it may be... more In collaborative systems involving a user and an agent working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can create costly user interruptions. One of the most important issue concerning the initiation of information sharing in collaborative systems is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been previously investigated, these works assumed either a large amount of information was available about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods with classi cation algorithms tools to create a fast learning algorithm, MICU. MICU exploits the similarities between users in order to learn from known users to new but similar users and therefore requires less information on each u...
A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can ... more A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can be used for modeling the static structure of a software system. Learning from errors is a teaching approach based on the assumption that errors can promote learning. We applied a constructive approach of using errors in designing a UML class diagram in order to (a) categorize the students’ errors when they design a class diagram from a text scenario that describes a specific organization and (b) determine whether the learning-from-errors approach enables students to produce more accurate and correct diagrams. The research was conducted with college students (N = 45) studying for their bachelor’s degree in engineering. The approach is presented, and the learning-fromerrors activity is illustrated. We present the students’ errors in designing the class diagram before and after the activity, together with the students’ opinions about applying the new approach in their course. Twenty errors ...
Interruptions can have a significant impact on users working to complete a task. When people are ... more Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often re-quire long training periods that make them unsuitable for on-line collaborative environments where new users frequently participate. In this paper, we present a novel synthesis between Collab-orative Filtering methods and machine learning classification algorithms to create a fast learning algorithm, CRISP. CRISP exploits the similarities between users in order to apply data from known users to new users, therefore requiring less in-formation on each person. Results from user studies indicate the algorithm significantly improves users ’ performances in completing the...
In teamwork when a user and an agent are working together on a joint task it may be important to ... more In teamwork when a user and an agent are working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can constitute costly user interruptions. One of the most important issue concerning the initiation of information sharing in teamwork is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been investigated in prior works, all of those works assumed either a large amount of information existed about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods together with classification algorithms tools in order to create a fast learning algorithm. This algorithm exploits the similarities between users in order to learn from known users to new but similar users and therefore demands less information on ea...
In collaborative systems involving a user and an agent working together on a joint task it may be... more In collaborative systems involving a user and an agent working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can create costly user interruptions. One of the most important issue concerning the initiation of information sharing in collaborative systems is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been previously investigated, these works assumed either a large amount of information was available about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods with classification algorithms tools to create a fast learning algorithm, MICU. MICU exploits the similarities between users in order to learn from known users to new but similar users and therefore requires less information on each user in compare to other methods. Experimental results indicate the algorithm significantly improves system performance even with a small amount of data on each user.
Coalition formation is a key topic in multi-agent systems (mas). Coalitions enable agents to achi... more Coalition formation is a key topic in multi-agent systems (mas). Coalitions enable agents to achieve goals that they may not have been able to achieve independently, and en- courages resource sharing among agents with different goals. A range of previous studies have found that problems in coalitional games tend to be computationally complex. However, such hardness results consider the entire input as one, ignoring any structural information on the instances. In the case of coalition formation problems, this bundles to- gether several distinct elements of the input, e.g. the agent set, the goal set, the resources, etc. In this paper we re- examine the complexity of coalition formation problems in the coalition resources game model, as a function of their distinct input elements, using the theory of parameterized complexity. The analysis shows that not all parts of the in- put are created equal, and that many instances of the prob- lem are actually tractable. We show that the problems are FPT in the number of goals, implying that if the number of goals is bounded then an efficient algorithm is available. Similarly, the problems are FPT in the combination of the number of agents and resources, again implying that if these parameters are bounded, then an efficient algorithm is avail- able. On the other hand, the problems are para- NP hard in the number of resources, implying that even if we bound the number of resources the problems (probably) remain hard. Additionally, we show that most problems are W(1)-hard in the size of the coalition of interest, indicating that there is (probably) no algorithm polynomial in all but the coalition size. The exact definitions of the parameterized complexity notions FPT, Para- NP and W(1) are provided herein.
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