Eugene Santos

• PhD
• Professor at Dartmouth College

This paper addresses the problem of both actively searching and tracking multiple unknown dynamic objects in a known environment with multiple cooperative autonomous agents with partial observability. The tracking of a target ends when the uncertainty is below a threshold. Current methods typically assume homogeneous agents without access to extern...

Many Reinforcement Learning algorithms assume a Markov reward function to guarantee optimality. However, not all reward functions are Markov. This paper proposes a framework for mapping non-Markov reward functions into equivalent Markov ones by learning specialized reward automata, Reward Machines. Unlike the general practice of learning Reward Mac...

There are many applications where an autonomous agent can perform many sets of actions. It must choose one set of actions based on some behavioral constraints on the agent. Past work has used deontic logic to declaratively express such constraints in logic, and developed the concept of a feasible status set (FSS), a set of actions that satisfy th...

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are...

Humans learn from both successful and unsuccessful experiences, because useful information about how to solve complex problems can be gleaned not only from success but also from failure. In this paper, we propose a method for investigating this difference by applying Preference based Inverse Reinforcement Learning to Double Transition Models built...

Successful machine learning methods require a trade-off between memorization and generalization. Too much memorization and the model cannot generalize to unobserved examples. Too much over-generalization and we risk under-fitting the data. While we commonly measure their performance through cross validation and accuracy metrics, how should these al...

Drone based terrorist attacks are increasing daily. It is not expected to be long before drones are used to carry out terror attacks in urban areas. We have developed the DUCK multi-agent testbed that security agencies can use to simulate drone-based attacks by diverse actors and develop a combination of surveillance camera, drone, and cyber defens...

Successful machine learning methods require a trade-off between memorization and generalization. Too much memorization and the model cannot generalize to unobserved examples. Too much over-generalization and we risk under-fitting the data. While we commonly measure their performance through cross validation and accuracy metrics, how should these al...

Inverse reinforcement learning (IRL) is a useful tool for building autonomous agents capable of making decisions by learning from the behavioral records of human decision-makers. Incorporating individual differences into multi-agent systems can be key to solving complex problems that are difficult or impossible for an individual agent or a monolith...

Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of dr...

The adoption of human-machine teams is rapidly expanding in many domains such as healthcare and disaster relief. Fueled by novel advances in robotics, artificial intelligence, and other technologies, machines with relatively high degrees of autonomy and self-awareness are being developed to improve efficiency and productivity in complex dynamic env...

During opinion formation, interacting agents can be assumed to be engaging in learning and decision-making processes to satisfy their individual goals. These goals are determined by the agents' preferences-which are often unknown, complex, and unpredictable. Most opinion formation frameworks however , assume static preferences and fail to model pra...

Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algori...

Harmony search algorithm with multi-parent crossover (HSA-MPC) is a hybrid algorithm that relies on benefiting from the crossover operation to combine more than one harmony to generate a new harmony. The picked harmonies are taken from an archive pool with best harmonies. In a previous study, the algorithm proves its efficiency when compared to oth...

Understanding the human decision-making process and evaluating the quality of these decisions has been the focus of many researchers. Previously, we proposed a computational, cognitive framework called the Double Transition Model (DTM) to study human decision-making processes. We applied it to simulate a couple of scenarios developed through a nava...

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuris...

Deception detection has been studied for hundreds of years. A particularly challenging problem is to not only identify truth from deception, but also discriminate misinformation, i.e. errors, from deception. Misinformation has generally been ignored in the study of deception detection, but through analysing the foundations of deception, it may be p...

Reasoning about the behavior of real-world systems and processes faces problems such as repeating events or subprocesses, evolving component behaviors, and indefinite time horizons. To date, existing representations of time and uncertainty have been unable to fully address such requirements. They often tradeoff assumptions of available knowledge ag...

Capturing human decision-making in complex environments is challenging due to uncertainty, high-stakes, and dynamic human factors. Most models fail to replicate human decision-making since they assume rationality and ignore contextual factors such as intuition and experience. Models that incorporate these elements are usually descriptive rather tha...

In Proactive Decision Support (PDS), the goal is to provide Commanders with the information that they need at the right time in order to make the right decision while dealing with a large expanse of contextual information about an uncertain and dynamic environment. Providing the Commander with too much information degrades their performance in time...

Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent) system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis r...

A Commander's decision making style represents how he weighs his choices and evaluates possible solutions with regards to his goals. Specifically, in the naval warfare domain, it relates the way he processes a large amount of information in dynamic, uncertain environments, allocates resources, and chooses appropriate actions to pursue. In this pape...

Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change,...

This paper addresses the fundamental research question: “How can we determine the sequential decision- making process inside a decision maker’s mind?” We construct a dynamic Markov Decision Process using a Double Transition Model (DTM). The DTM is a cognitive model decomposing the decision-making process into episodic tasks that are extracted from...

We present a new text-to-image re-ranking approach for improving the relevancy rate in searches. In particular, we focus on the fundamental semantic gap that exists between the low-level visual features of the image and high-level textual queries by dynamically maintaining a connected hierarchy in the form of a concept database. For each textual qu...

Computational social science methodologies are increasingly being viewed as critical for modeling complex individual and organizational behaviors in dynamic, real world scenarios. However, many challenges for identifying, representing and incorporating appropriate socio-cultural behaviors remain. Social theories provide rules, which have strong the...

Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, mo...

Opinion dynamics is a complex procedure that entails a cognitive process when dealing with how a person integrates influential opinions to form a revised opinion. In this work, we present a new approach to model opinion dynamics by treating the opinion on an issue as a product inferred from one's knowledge bases, where the knowledge bases keep grow...

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typi...

Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters...

A user is an important factor that contributes to the success or failure of any information retrieval system. Unfortunately, users often do not have the same technical and/or domain knowledge as the designers of such a system, while the designers are often limited in their understanding of a target user’s needs. In this chapter, we study the proble...

Independence-based (IB) assignments to Bayesian belief networks were originally proposed as abductive explanations. IB assignments assign fewer variables in abductive explanations than do schemes assigning values to all evidentially supported variables. We use IB assignments to approximate marginal probabilities in Bayesian belief networks. Recent...

Belief updating in Bayes nets, a well known computationally hard problem, has recently been approximated by several deterministic algorithms, and by various randomized approximation algorithms. Deterministic algorithms usually provide probability bounds, but have an exponential runtime. Some randomized schemes have a polynomial runtime, but provide...

Bayesian knowledge bases (BKBs) are a generalization of Bayes networks and weighted proof graphs (WAODAGs), that allow cycles in the causal graph. Reasoning in BKBs requires finding the most probable inferences consistent with the evidence. The cost-sharing heuristic for finding least-cost explanations in WAODAGs was presented and shown to be effec...

our transaction?????????s name has changed - from IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS to a more concise Transactions on Cybernetics (TCyb). For the past many months, the publications of the IEEE Systems, Man, and Cybernetics Society (SMCS) have been undergoing a major reorganization. The reorganization was based o...

Healthcare situations are ever increasingly complex: team performance can easily deteriorate when medical procedures are delivered by teams composed of individuals having different intentions. In fact, medical errors resulting in catastrophic outcomes are often due to the conflicting goals, plans, or intentions among those individuals who make up t...

An important task of modeling complex social behaviors is to observe and understand individual/group beliefs and attitudes. These beliefs, however, are not stable and may change multiple times as people gain additional information/perceptions from various external sources, which in turn, may affect their subsequent behavior. To detect and track suc...

We are working on the problem of developing a flexible, generic metal earning process that supports algorithm selection based on studying the algorithms' past performance behaviors. State of the art machine learning systems display limitations in that they require a great deal of human supervision to select an effective algorithm with corresponding...

Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its...

Many applications require knowledge about how to deceive, including those related to safety, security, and warfare. Speech and text analysis can help detect deception, as can cameras, microphones, physiological sensors, and intelligent software. Models of deception and noncooperation can make a virtual or mixed-reality training environment more rea...

Research in Information Retrieval (IR) experienced a paradigm shift from first having too few documents to search from to now having way too many of them. When users have trouble finding relevant documents, they tend to become frustrated and give up searching. Scholars have attempted to reduce instances of search frustration via query expansion, in...

The major focus in the field of modeling & simulation for network centric environments has been on the physical layer while making simplifications for the human-in-the-loop. However, the human element has a big impact on the capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team building,...

Research on deception detection has been mainly focused on two kinds of approaches. In one, people consider deception types and taxonomies, and use different counter strategies to detect and reverse deception. In the other, people search for verbal and non-verbal cues in the content of deceptive communication. However, general theories that study f...

Distributed systems based on cooperative multi-agents have been used in a wide range of application domains. However, the need for real-time processing in large and dynamic search spaces has led to new challenges. In addition to the constraints in time and computational resources, the agents have to operate under highly dynamic conditions in comple...

A person's beliefs and attitudes may change multiple times as they gain additional information/perceptions from various external sources, which in turn, may affect their subsequent behavior. Such influential sources, however, are often invisible to the public due to a variety of reasons - private communications, what one randomly reads or hears, an...

One of the main problems facing human analysts dealing with large amounts of dynamic data is that important information may not be assessed in time to aid the decision making process. We present a novel distributed processing framework called Intelligent Foraging, Gathering and Matching (I-FGM) that addresses this problem by concentrating on resour...

We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the pr...

Modeling complex real world scenarios require representing and analyzing information from multiple domains including social, economic and political aspects. However, most of the current frameworks in social networks are not generic enough to incorporate multi-domain information or to be applied in different scenarios. Current frameworks also make s...

Knowledge acquisition is an essential process in improving the problem-solving capabilities of existing knowledge-based systems through the absorption of new information and facilitating change in current knowledge. However, without a verification mechanism, these changes could result in violations of semantic soundness of the knowledge causing inc...

Modeling real-world social situations has proven to be one of the most daunting challenges in computational social science. With the exception of simplistic, single-domain scenarios, most computational models are quickly overwhelmed with the complexity and diversity of real-world scenarios. In this paper, we apply intent-driven modeling to a comple...

Uncertainty handling for semantic networks is a difficult problem which has slowed the effort to fully develop a semantic web. Uncertainty handling becomes particularly challenging when incompleteness is present in a domain, as it frequently is when modeling real-world complexity. To date, work on uncertainty frameworks for semantic networks has no...

IntroductionRelated WorkTeam PerformanceSurgical Intent InferencingEmpirical StudyConclusion Key TermsReferences

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typi...

A user's cognitive style has been found to affect how they search for information, how they analyze the information, and how they make decisions in an analytical process. In this paper, we propose an approach that uses Hidden Markov Models (HMM) to dynamically capture a user's cognitive style by automatically exploring the sequence of actions and r...

Deception detection plays an important role in safely and reliably using multientity advisory models such as multiagent intelligence systems. The benevolence assumption people have based their implementations of multiagent (human and/or synthetic) systems on is rarely valid in the real world. Unfortunately, deception detection is extremely challeng...

Homeland security and disaster relief are some of the critical areas of E-governance that have to deal with vast amounts of dynamic heterogeneous data. Providing rapid real-time search capabilities for such applications is a challenge. Intelligent Foraging, Gathering, and Matching (I-FGM) is an established framework developed to assist users to fin...

Time is the key stimulus to change, causality and interaction which are the main components of a dynamic world. Therefore, the modeling of knowledge, especially in complex and dynamic domains like economics, sociology, and ecology, must incorporate the concept of time. Although there has been much research over the years on the representation of kn...

One of the greatest challenges in accurately modeling a human system is the integration of dynamic, fine-grained information in a meaningful way. A model must allow for reasoning in the face of uncertain and incomplete information and be able to provide an easy to understand explanation of why the system is behaving as it is. To date, work in multi...

The research team during the CASE project was working on the problem of modeling an analyst's intent in order to improve tacit collaboration among analysts. The actions of an analyst are captured, and their goals and commitment to achieve them are inferred in order to improve effectiveness in collaborative tasks by building a user model to predict...

We are working on the problem of modeling an analyst’s intent in order to improve collaboration among intelligence analysts. Our approach is to infer the analyst’s goals, commitment, and actions to improve the effectiveness of collaboration. This is a crucial problem to ensure successful collaboration because analyst intent provides a deeper unders...

One of the biggest challenges for intelligence analysts who participate in prevention or response to a terrorism act is to quickly find relevant information from massive amount of data. Along with research on information retrieval and filtering, text summarization is an effective technique to help intelligent analysts shorten their time to find cri...

To foster shared battlespace awareness among air strategy planners, BAE Systems has developed Commander's Model Integration and Simulation Toolkit (CMIST), an Integrated Development Environment for authoring, integration, validation, and debugging of models relating multiple domains, including political, military, social, economic and information....

We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the pr...

In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a user's intent is captured and used for improving his/her effectiveness in an information seeking task. The user intent is captured by analyzing the commonality of the retrieved relevant documents. The effect...

With the proliferation of the Internet and rapid development of information and communication infrastructure, E-governance has become a viable option for effective deployment of government services and programs. Areas of E-governance such as Homeland security and disaster relief have to deal with vast amounts of dynamic heterogeneous data. Providin...

When decisions need to be made in government, the intelligence community (IC) is tasked with analyzing the situation. This analysis is based on a huge amount of information and usually under severe time constraints. As such, it is particularly vulnerable to attacks from insiders with malicious intent. A malicious insider may alter, fabricate, or hi...

Along with research on information retrieval and filtering, text summarization is an effective technique to help users save time in finding critical information and making a timely decision. The quality of a summary may improve a user's performance in an information seeking task if it includes the relevant information that he/she is looking for in...

Working with the DUC 2002 collection for multi- document summarization, we considered two types of document sets: sets consisting of closely correlated documents with highly overlapped content; and sets of diverse documents covering a wide scope of topics. Intuitively, this suggests that creating a quality summary would be more difficult for the la...

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Deception detection plays an important role in the military decision-making process, but detecting deception is a challenging task. The deception planning process involves a number of human factors. It is intent-driven where intentions are usually hidden or not easily observable. As a result, in order to detect deception, any adversary model must h...

When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native conformation remains a computationally hard problem -even in simplified models such as hydrophobic-hydrophilic (HP)...

Understanding the intent of today's enemy necessitates changes in intelligence collection, processing, and dissemination. Unlike cold war antagonists, today's enemies operate in small, agile, and distributed cells whose tactics do not map well to established doctrine. This has necessitated a proliferation of advanced sensor and intelligence gatheri...

In its grandest sense, Project CASIE explored the development of a computational system capable of high level perception and problem solving that reflects the cognitive processes of the human brain. Most specifically, it concentrated on better understanding and modeling intuition and insight in a computational fashion. The goal was to address the f...

Knowledge validation, as part of knowledge base verification and validation, is a critical process in knowledge engineering. The ultimate goal of this process is to make the knowledge base satisfy all test cases given by human experts. This is further complicated by factors such as uncertainty and incompleteness. Our paper covers theoretical result...

Homeland securiEy and disaster reliefore some oflhe crilical Uri'US o/t.:-gol'ernance Ihat have to deal with vast amounts ofdynamic heterogeneous Jala. Providing rapid reul-lime search capobilitiesjor such (Ippli­ calions is a challenge. Intelligent Foraging, Galhering, and .\latching (I-FGM) is an estublishedji"Wllell'ork developed to assistllsers...

One of the biggest challenges in providing realistic soft- ware agent models lies in taking into account the soft fac- tors (or human factors), such as religious, social, political, psychological and economic factors, which are a critical part of people's decision making and behavior. Whether such agents are used in a simulation or to support analy...

Accounting for social, cultural, and political factors must form the basis for understanding decision-making, actions, and reactions of individuals, thus driving their behaviors and intentions. Clearly, the individual is not wholly defined by just personal social, cultural, and political beliefs but also functions within a group of individuals. Wit...

Accounting for social, cultural, and political factors must form the basis for understanding decision-making, actions, and reactions of individuals, thus driving their behaviors and intentions. Clearly, the individual is not wholly defined by just personal social, cultural, and political beliefs but also functions within a group of individuals. Wit...

To quickly find relevant information from huge amounts of data is a very challenging issue for intelligence analysts. Most employ their prior domain knowledge to improve their process of finding relevant information. In this paper, we explore the influences of a user's prior domain knowledge on the effectiveness of an information seeking task by us...

Intelligent Foraging, Gathering and Matching (I-FGM) combines a unique multi-agent architecture with a novel partial processing paradigm to provide a solution for real-time information retrieval in large and dynamic databases. I-FGM provides a unified framework for combining the results from various heterogeneous databases and seeks to provide easi...

Nowadays, there is an increasing demand for the military to conduct operations that are beyond traditional warfare. In these operations, analyzing and understanding those who are involved in the situation, how they are going to behave, and why they behave in certain ways is critical for success. The challenge lies in that behavior does not simply f...