Tamal Biswas | SUNY Buffalo, Singapore (original) (raw)
Papers by Tamal Biswas
Research on judging decisions made by fallible (human) agents is not as much advanced as research... more Research on judging decisions made by fallible (human) agents is not as much advanced as research on finding optimal decisions. Human decisions are often influenced by various factors, such as risk, uncertainty, time pressure, and depth of cognitive capability, whereas decisions by an intelligent agent (IA) can be effectively optimal without these limitations. The concept of 'depth', a well-defined term in game theory (including chess), does not have a clear formulation in decision theory. To quantify 'depth' in decision theory, we can configure an IA of supreme competence to 'think' at depths beyond the capability of any human, and in the process collect evaluations of decisions at various depths. One research goal is to create an intrinsic measure of the depth of thinking required to answer certain test questions, toward a reliable means of assessing their difficulty apart from item-response statistics. We relate the depth of cognition by humans to depths of search, and use this information to infer the quality of decisions made, so as to judge the decision-maker from his decisions. We use large data from real chess tournaments and evaluations from chess programs (AI agents) of strength beyond all human players. We then seek to transfer the results to other decision-making fields in which effectively optimal judgments can be obtained from either hindsight, answer banks, powerful AI agents or from answers provided by judges of various competency.
In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient w... more In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient way of preventing hazardous situations on roads plays an important role in the successful deployment of the system. Trust or reputation models often need to be integrated with inter-vehicle communication protocols in use to avoid selfish or malicious behavior by the vehicles exploiting the system. Existing trust and reputation models for VNs lack the capability of fast and accurate trust management suitable for the ephemeral association of vehicles. In this paper, we design an augmented trust model for VNs by assigning each vehicle a long term trust value. Our model eliminates the overhead of repeated bootstrapping and ensures accountability of the vehicles for incident reporting and other critical actions. The two main features of our framework, viz. a three- party authentication and privacy protocol, and a trust propagation model, both of which are crucial for a successful deployment of VNs, can also be used in any generic security application.
Distributional analysis of large data-sets of chess games played by humans and those played by co... more Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance: (1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers; (2) For almost any degree of advantage or disadvantage, a human player has a significant 2–3% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers. (3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer freestyle tandems. The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed. Other regularities of human and computer performances are describe...
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
Inferences about structured patterns in human decision making have been drawn from medium-scale s... more Inferences about structured patterns in human decision making have been drawn from medium-scale simulated competitions with human subjects. The concepts analyzed in these studies include level-k thinking, satisficing, and other human error tendencies. These concepts can be mapped via a natural depth of search metric into the domain of chess, where copious data is available from hundreds of thousands of games by players of a wide range of precisely known skill levels in real competitions. The games are analyzed by strong chess programs to produce authoritative utility values for move decision options by progressive deepening of search. Our experiments show a significant relationship between the formulations of level-k thinking and the skill level of players. Notably, the players are distinguished solely on moves where they erred-according to the average depth level at which their errors are exposed by the authoritative analysis. Our results also indicate that the decisions are often independent of tail assumptions on higher-order beliefs. Further, we observe changes in this relationship in different contexts, such as minimal versus acute time pressure. We try to relate satisficing to insufficient level of reasoning and answer numerically the question, why do humans blunder?
2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016
In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient w... more In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient way of preventing hazardous situations on roads plays an important role in the successful deployment of the system. Trust or reputation models often need to be integrated with inter-vehicle communication protocols in use to avoid selfish or malicious behavior by the vehicles exploiting the system. Existing trust and reputation models for VNs lack the capability of fast and accurate trust management suitable for the ephemeral association of vehicles. In this paper, we design an augmented trust model for VNs by assigning each vehicle a long term trust value. Our model eliminates the overhead of repeated bootstrapping and ensures accountability of the vehicles for incident reporting and other critical actions. The two main features of our framework, viz. a three- party authentication and privacy protocol, and a trust propagation model, both of which are crucial for a successful deployment of VNs, can also be used in any generic security application.
Lecture Notes in Computer Science, 2015
Theoretical Computer Science, 2015
This paper proposes strategies for maintaining a database of computational results of functions f... more This paper proposes strategies for maintaining a database of computational results of functions f on sequence arguments x, where x is sorted in non-decreasing order and f (x) has greatest dependence on the first few terms of x. This scenario applies also to symmetric functions f , where the partial derivatives approach zero as the corresponding component value increases. The goal is to pre-compute exact values f (u) on a tight enough net of sequence arguments, so that given any other sequence x, a neighboring sequence u in the net giving a close approximation can be efficiently found. Our scheme avoids pre-computing the more-numerous partial-derivative values. It employs a new data structure that combines ideas of a trie and an array implementation of a heap, representing grid values compactly in the array, yet still allowing access by a single index lookup rather than pointer jumping. We demonstrate good size/approximation performance in a natural application.
Proceedings of the International Conference on Agents and Artificial Intelligence, 2015
Qualitative approaches to cognitive rigor and depth and complexity are broadly represented by Web... more Qualitative approaches to cognitive rigor and depth and complexity are broadly represented by Webb's Depth of Knowledge and Bloom's Taxonomy. Quantitative approaches have been relatively scant, and some have been based on ancillary measures such as the thinking time expended to answer test items. In competitive chess and other games amenable to incremental search and expert evaluation of options, we show how depth and complexity can be quantified naturally. We synthesize our depth and complexity metrics for chess into measures of difficulty and discrimination, and analyze thousands of games played by humans and computers by these metrics. We show the extent to which human players of various skill levels evince shallow versus deep thinking, and how they cope with 'difficult' versus 'easy' move decisions. The goal is to transfer these measures and results to application areas such as multiple-choice testing that enjoy a close correspondence in form and item values to the problem of finding good moves in chess positions.
Proceedings of the International Conference on Agents and Artificial Intelligence, 2015
2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013
We build a model for the kind of decision making involved in games of strategy such as chess, mak... more We build a model for the kind of decision making involved in games of strategy such as chess, making it abstract enough to remove essentially all game-specific contingency, and compare it to known psychometric models of test taking, item response, and performance assessment. Decisions are modeled in terms of fallible agents Z faced with possible actions ai whose utilities ui = u(ai) are not fully apparent. The three main goals of the model are prediction, meaning to infer probabilities pi for Z to choose ai; intrinsic rating, meaning to assess the skill of a person's actual choices ai t over various test items t; and simulation of the distribution of choices by an agent with a specified skill set. We describe and train the model on large data from chess tournament games of different ranks of players, and exemplify its accuracy by applying it to give intrinsic ratings for world championship matches.
Lecture Notes in Computer Science, 2014
Lecture Notes in Computer Science, 2015
The assessment of chess players is both an increasingly attractive opportunity and an unfortunate... more The assessment of chess players is both an increasingly attractive opportunity and an unfortunate necessity. The chess community needs to limit potential reputational damage by inhibiting cheating and unjustified accusations of cheating: there has been a recent rise in both. A number of counter-intuitive discoveries have been made by benchmarking the intrinsic merit of players' moves: these call for further investigation. Is Capablanca actually, objectively the most accurate World Champion? Has ELO rating inflation not taken place? Stimulated by FIDE/ACP, we revisit the fundamentals of the subject to advance a framework suitable for improved standards of computational experiment and more precise results. Other games and domains look to chess as demonstrator of good practice, including the rating of professionals making high-value decisions under pressure, personnel evaluation by Multichoice Assessment and the organization of crowd-sourcing in citizen science projects. The '3P' themes of performance, prediction and profiling pervade all these domains.
Research on judging decisions made by fallible (human) agents is not as much advanced as research... more Research on judging decisions made by fallible (human) agents is not as much advanced as research on finding optimal decisions. Human decisions are often influenced by various factors, such as risk, uncertainty, time pressure, and depth of cognitive capability, whereas decisions by an intelligent agent (IA) can be effectively optimal without these limitations. The concept of 'depth', a well-defined term in game theory (including chess), does not have a clear formulation in decision theory. To quantify 'depth' in decision theory, we can configure an IA of supreme competence to 'think' at depths beyond the capability of any human, and in the process collect evaluations of decisions at various depths. One research goal is to create an intrinsic measure of the depth of thinking required to answer certain test questions, toward a reliable means of assessing their difficulty apart from item-response statistics. We relate the depth of cognition by humans to depths of search, and use this information to infer the quality of decisions made, so as to judge the decision-maker from his decisions. We use large data from real chess tournaments and evaluations from chess programs (AI agents) of strength beyond all human players. We then seek to transfer the results to other decision-making fields in which effectively optimal judgments can be obtained from either hindsight, answer banks, powerful AI agents or from answers provided by judges of various competency.
In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient w... more In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient way of preventing hazardous situations on roads plays an important role in the successful deployment of the system. Trust or reputation models often need to be integrated with inter-vehicle communication protocols in use to avoid selfish or malicious behavior by the vehicles exploiting the system. Existing trust and reputation models for VNs lack the capability of fast and accurate trust management suitable for the ephemeral association of vehicles. In this paper, we design an augmented trust model for VNs by assigning each vehicle a long term trust value. Our model eliminates the overhead of repeated bootstrapping and ensures accountability of the vehicles for incident reporting and other critical actions. The two main features of our framework, viz. a three- party authentication and privacy protocol, and a trust propagation model, both of which are crucial for a successful deployment of VNs, can also be used in any generic security application.
Distributional analysis of large data-sets of chess games played by humans and those played by co... more Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance: (1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers; (2) For almost any degree of advantage or disadvantage, a human player has a significant 2–3% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers. (3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer freestyle tandems. The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed. Other regularities of human and computer performances are describe...
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
Inferences about structured patterns in human decision making have been drawn from medium-scale s... more Inferences about structured patterns in human decision making have been drawn from medium-scale simulated competitions with human subjects. The concepts analyzed in these studies include level-k thinking, satisficing, and other human error tendencies. These concepts can be mapped via a natural depth of search metric into the domain of chess, where copious data is available from hundreds of thousands of games by players of a wide range of precisely known skill levels in real competitions. The games are analyzed by strong chess programs to produce authoritative utility values for move decision options by progressive deepening of search. Our experiments show a significant relationship between the formulations of level-k thinking and the skill level of players. Notably, the players are distinguished solely on moves where they erred-according to the average depth level at which their errors are exposed by the authoritative analysis. Our results also indicate that the decisions are often independent of tail assumptions on higher-order beliefs. Further, we observe changes in this relationship in different contexts, such as minimal versus acute time pressure. We try to relate satisficing to insufficient level of reasoning and answer numerically the question, why do humans blunder?
2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016
In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient w... more In vehicular networks (VN), the response time is critical, whereas, an autonomous and efficient way of preventing hazardous situations on roads plays an important role in the successful deployment of the system. Trust or reputation models often need to be integrated with inter-vehicle communication protocols in use to avoid selfish or malicious behavior by the vehicles exploiting the system. Existing trust and reputation models for VNs lack the capability of fast and accurate trust management suitable for the ephemeral association of vehicles. In this paper, we design an augmented trust model for VNs by assigning each vehicle a long term trust value. Our model eliminates the overhead of repeated bootstrapping and ensures accountability of the vehicles for incident reporting and other critical actions. The two main features of our framework, viz. a three- party authentication and privacy protocol, and a trust propagation model, both of which are crucial for a successful deployment of VNs, can also be used in any generic security application.
Lecture Notes in Computer Science, 2015
Theoretical Computer Science, 2015
This paper proposes strategies for maintaining a database of computational results of functions f... more This paper proposes strategies for maintaining a database of computational results of functions f on sequence arguments x, where x is sorted in non-decreasing order and f (x) has greatest dependence on the first few terms of x. This scenario applies also to symmetric functions f , where the partial derivatives approach zero as the corresponding component value increases. The goal is to pre-compute exact values f (u) on a tight enough net of sequence arguments, so that given any other sequence x, a neighboring sequence u in the net giving a close approximation can be efficiently found. Our scheme avoids pre-computing the more-numerous partial-derivative values. It employs a new data structure that combines ideas of a trie and an array implementation of a heap, representing grid values compactly in the array, yet still allowing access by a single index lookup rather than pointer jumping. We demonstrate good size/approximation performance in a natural application.
Proceedings of the International Conference on Agents and Artificial Intelligence, 2015
Qualitative approaches to cognitive rigor and depth and complexity are broadly represented by Web... more Qualitative approaches to cognitive rigor and depth and complexity are broadly represented by Webb's Depth of Knowledge and Bloom's Taxonomy. Quantitative approaches have been relatively scant, and some have been based on ancillary measures such as the thinking time expended to answer test items. In competitive chess and other games amenable to incremental search and expert evaluation of options, we show how depth and complexity can be quantified naturally. We synthesize our depth and complexity metrics for chess into measures of difficulty and discrimination, and analyze thousands of games played by humans and computers by these metrics. We show the extent to which human players of various skill levels evince shallow versus deep thinking, and how they cope with 'difficult' versus 'easy' move decisions. The goal is to transfer these measures and results to application areas such as multiple-choice testing that enjoy a close correspondence in form and item values to the problem of finding good moves in chess positions.
Proceedings of the International Conference on Agents and Artificial Intelligence, 2015
2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013
We build a model for the kind of decision making involved in games of strategy such as chess, mak... more We build a model for the kind of decision making involved in games of strategy such as chess, making it abstract enough to remove essentially all game-specific contingency, and compare it to known psychometric models of test taking, item response, and performance assessment. Decisions are modeled in terms of fallible agents Z faced with possible actions ai whose utilities ui = u(ai) are not fully apparent. The three main goals of the model are prediction, meaning to infer probabilities pi for Z to choose ai; intrinsic rating, meaning to assess the skill of a person's actual choices ai t over various test items t; and simulation of the distribution of choices by an agent with a specified skill set. We describe and train the model on large data from chess tournament games of different ranks of players, and exemplify its accuracy by applying it to give intrinsic ratings for world championship matches.
Lecture Notes in Computer Science, 2014
Lecture Notes in Computer Science, 2015
The assessment of chess players is both an increasingly attractive opportunity and an unfortunate... more The assessment of chess players is both an increasingly attractive opportunity and an unfortunate necessity. The chess community needs to limit potential reputational damage by inhibiting cheating and unjustified accusations of cheating: there has been a recent rise in both. A number of counter-intuitive discoveries have been made by benchmarking the intrinsic merit of players' moves: these call for further investigation. Is Capablanca actually, objectively the most accurate World Champion? Has ELO rating inflation not taken place? Stimulated by FIDE/ACP, we revisit the fundamentals of the subject to advance a framework suitable for improved standards of computational experiment and more precise results. Other games and domains look to chess as demonstrator of good practice, including the rating of professionals making high-value decisions under pressure, personnel evaluation by Multichoice Assessment and the organization of crowd-sourcing in citizen science projects. The '3P' themes of performance, prediction and profiling pervade all these domains.