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Title: An efficient method for representing and computing transitive closure over temporal relations.
Creator: Kovarik, Vincent J., Gonzalez, Avelino, Engineering
Abstract / Description: University of Central Florida College of Engineering Thesis; The need for temporal reasoning is found throughout the engineering disciplines. James Allen introduced a representation for temporal reasoning based upon the concept of intervals. This approach provides a rich set of temporal relations for reasoning over events and changes in state. The full temporal algebra is NP-complete however. The algorithm developed by Allen executes in 0(n3) time but only ensures consistency between any...
Show moreUniversity of Central Florida College of Engineering Thesis; The need for temporal reasoning is found throughout the engineering disciplines. James Allen introduced a representation for temporal reasoning based upon the concept of intervals. This approach provides a rich set of temporal relations for reasoning over events and changes in state. The full temporal algebra is NP-complete however. The algorithm developed by Allen executes in 0(n3) time but only ensures consistency between any three intervals. This research presents an approach to representing interval relations as a bit-encoded form which captures the relationships between the end-points of the intervals. A bit-algebra is then defined which provides an algorithmic method for computing transitive relations without requiring the table lookup of Allen's algorithm. By reducing the set of ambiguous interval representations to the set of relationships which have unknown temporal extent, a robust subset of the full algebra is defined which maintains the direct computation of transitive relationships.
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Date Issued: 1994
Identifier: CFR0001859, ucf:52919
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFR0001859

Title: Semantic correlation of behavior for the interoperability of heterogeneous simulations.
Creator: Dean, Christopher James, Gonzalez, Avelino J., Engineering
Abstract / Description: University of Central Florida College of Engineering Thesis; A desirable goal of military simulation training is to provide large scale or joint exercises to train personnel at higher echelons. To help meet this goal, many of the lower echelon combatants must consist of computer generated forces with some of these echelons composed of units from different simulations. The object of the research described is to correlate the behaviors of entities in different simulations so that they can...
Show moreUniversity of Central Florida College of Engineering Thesis; A desirable goal of military simulation training is to provide large scale or joint exercises to train personnel at higher echelons. To help meet this goal, many of the lower echelon combatants must consist of computer generated forces with some of these echelons composed of units from different simulations. The object of the research described is to correlate the behaviors of entities in different simulations so that they can interoperate with one another to support simulation training. Specific source behaviors can be translated to a form in terms of general behaviors which can then be correlated to any desired specific destination simulation behavior without prior knowledge of the pairing. The correlation, however, does not result in 100% effectiveness because most simulations have different semantics and were designed for different training needs. An ontology of general behaviors and behavior parameters, a database of source behaviors written in terms of these general behaviors with a database of destination behaviors. This comparison is based upon the similarity of sub-behaviors and the behavior parameters. Source behaviors/parameters may be deemed similar based upon their sub-behaviors or sub-parameters and their relationship (more specific or more general) to destination behaviors/parameters. As an additional constraint for correlation, a conversion path from all required destination parameters to a souce parameter must be found in order for the behavior to be correlated and thus executed. The length of this conversion path often determines the similarity for behavior parameters, both source and destination. This research has shown, through a set of experiments, that heuristic metrics, in conjunction with a corresponding behavior and parameter ontology, are sufficient for the correlation of heterogeneous simulation behavior. These metrics successfully correlated known pairings provided by experts and provided reasonable correlations for behaviors that have no corresponding destination behavior. For different simulations, these metrics serve as a foundation for more complex methods of behavior correlation.
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Date Issued: 1996
Identifier: CFR0008169, ucf:53071
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFR0008169

Title: EVOLVING MODELS FROM OBSERVED HUMAN PERFORMANCE.
Creator: Fernlund, Hans Karl Gustav, Gonzalez, Avelino J., University of Central Florida
Abstract / Description: To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical...
Show moreTo create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.
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Date Issued: 2004
Identifier: CFE0000013, ucf:46068
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000013

Title: COLLABORATIVE CONTEXT-BASED REASONING.
Creator: Barrett, Gilbert, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This dissertation explores modeling collaborative behavior, based on Joint Intentions Theory (JIT), in Context-Based Reasoning (CxBR). Context-Based Reasoning is one of several contextual reasoning paradigms. And, Joint Intentions Theory is the definitive semantic framework for collaborative behaviors. In order to formalize collaborative behaviors in CxBR based on JIT, CxBR is first described in terms of the more popular Belief, Desire, and Intention (BDI) model. Once this description is...
Show moreThis dissertation explores modeling collaborative behavior, based on Joint Intentions Theory (JIT), in Context-Based Reasoning (CxBR). Context-Based Reasoning is one of several contextual reasoning paradigms. And, Joint Intentions Theory is the definitive semantic framework for collaborative behaviors. In order to formalize collaborative behaviors in CxBR based on JIT, CxBR is first described in terms of the more popular Belief, Desire, and Intention (BDI) model. Once this description is established JIT is used as a basis for the formalism for collaborative behavior in CxBR. The hypothesis of this dissertation is that this formalism allows for effective collaborative behaviors in CxBR. Additionally, it is also hypothesized that CxBR agents inferring intention from explicitly communicating Contexts allows for more efficient modeling of collaborative behaviors than inferring intention from situational awareness. Four prototypes are built and evaluated to test the hypothesis and the evaluations are favorable. Effective collaboration is demonstrated through cognitive task analysis and through metrics based on JIT definitions. Efficiency is shown through software metric evaluations for volume and complexity of code.
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Date Issued: 2007
Identifier: CFE0001667, ucf:47198
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001667

Title: LEARNING HUMAN BEHAVIOR FROM OBSERVATION FOR GAMING APPLICATIONS.
Creator: Moriarty, Christopher, Gonzalez, Avelino, University of Central Florida
Abstract / Description: The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in....
Show moreThe gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value.
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Date Issued: 2007
Identifier: CFE0001694, ucf:47201
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001694

Title: ON ADVANCED TEMPLATE-BASED INTERPRETATION AS APPLIED TO INTENTION RECOGNITION IN A STRATEGIC ENVIRONMENT.
Creator: Akridge, Cameron, Gonzalez, Avelino, University of Central Florida
Abstract / Description: An area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection...
Show moreAn area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection of tactics that it could not detect without somehow manipulating the environment. Therefore, the aim of this thesis is to address the problem of recognizing an opponent's intent in a strategic environment where the system can think ahead in time to see the agent's plan. To approach the problem, a structured form of knowledge called Template-Based Interpretation is borrowed from the work of others and enhanced to reason in a temporally dynamic simulation.
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Date Issued: 2007
Identifier: CFE0001517, ucf:47146
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001517

Title: AUTOMATED SCENARIO GENERATION SYSTEM IN A SIMULATION.
Creator: Tomizawa, Hajime, Gonzalez, Avelino, University of Central Florida
Abstract / Description: Developing training scenarios that induce a trainee to utilize specific skills is one of the facets of simulation-based training that requires significant effort. Simulation-based training systems have become more complex in recent years. Because of this added complexity, the amount of effort required to generate and maintain training scenarios has increased. This thesis describes an investigation into automating the scenario generation process. The Automated Scenario Generation System (ASGS)...
Show moreDeveloping training scenarios that induce a trainee to utilize specific skills is one of the facets of simulation-based training that requires significant effort. Simulation-based training systems have become more complex in recent years. Because of this added complexity, the amount of effort required to generate and maintain training scenarios has increased. This thesis describes an investigation into automating the scenario generation process. The Automated Scenario Generation System (ASGS) generates expected action flow as contexts in chronological order from several events and tasks with estimated time for the entire training mission. When the training objectives and conditions are defined, the ASGS will automatically generate a scenario, with some randomization to ensure no two equivalent scenarios are identical. This makes it possible to train different groups of trainees sequentially who may have the same level or training objectives without using a single scenario repeatedly. The thesis describes the prototype ASGS and the evaluation results are described and discussed. SVSTM Desktop is used as the development infrastructure for ASGS as prototype training system.
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Date Issued: 2006
Identifier: CFE0001336, ucf:47002
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001336

Title: A REINFORCEMENT LEARNING TECHNIQUE FOR ENHANCING HUMAN BEHAVIOR MODELS IN A CONTEXT-BASED ARCHITECTURE.
Creator: Aihe, David, Gonzalez, Avelino, University of Central Florida
Abstract / Description: A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject...
Show moreA reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly.
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Date Issued: 2008
Identifier: CFE0002466, ucf:47715
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002466

Title: A COMPARATIVE ANALYSIS BETWEEN CONTEXT-BASED REASONING (CXBR) AND CONTEXTUAL GRAPHS (CXGS).
Creator: Lorins, Peterson, Gonzalez, Avelino, University of Central Florida
Abstract / Description: Context-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a context sensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs...
Show moreContext-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a context sensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs use a continuous context-based representation for a given problem-solving scenario for decision-support processes. Both formalisms use contexts dynamically by continuously changing between necessary contexts as needed in appropriate instances. This thesis identifies a synergy between these two formalisms by looking into their similarities and differences. It became clear during the research that each paradigm was designed with a very specific family of problems in mind. Thus, CXBR best implements models of autonomous agents in environment, while CxGs is best implemented in a decision support setting that requires the development of decision-making procedures. Cross applications were implemented on each and the results are discussed.
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Date Issued: 2005
Identifier: CFE0000577, ucf:46433
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000577

Title: FAMTILE: AN ALGORITHM FOR LEARNING HIGH-LEVEL TACTICAL BEHAVIOR FROM OBSERVATION.
Creator: Stensrud, Brian, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level...
Show moreThis research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level context sequence executed by the expert. To learn this sequence, this research proposes FAMTILE - the Fuzzy ARTMAP / Template-Based Interpretation Learning Engine. This algorithm attempts to achieve this learning task by constructing rules that govern the low-level context transitions made by the expert. By combining these rules with models for these low-level context behaviors, it is hypothesized that an intelligent model for the expert can be created that can adequately model his behavior. To evaluate FAMTILE, four testing scenarios were developed that attempt to achieve three distinct evaluation goals: assessing the learning capabilities of Fuzzy ARTMAP, evaluating the ability of FAMTILE to correctly predict expert actions and context choices given an observation, and creating a model of the expert's behavior that can perform the high-level task at a comparable level of proficiency.
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Date Issued: 2005
Identifier: CFE0000503, ucf:46455
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000503

Title: EXPLANATIONS IN CONTEXTUAL GRAPHS:A SOLUTION TO ACCOUNTABILITY INKNOWLEDGE BASED SYSTEMS.
Creator: Sherwell, Brian, Gonzalez, Avelino, University of Central Florida
Abstract / Description: In order for intelligent systems to be a viable and utilized tool, a user must be able to understand how the system comes to a decision. Without understanding how the system arrived at an answer, a user will be less likely to trust its decision. One way to increase a user's understanding of how the system functions is by employing explanations to account for the output produced. There have been attempts to explain intelligent systems over the past three decades. However, each attempt has had...
Show moreIn order for intelligent systems to be a viable and utilized tool, a user must be able to understand how the system comes to a decision. Without understanding how the system arrived at an answer, a user will be less likely to trust its decision. One way to increase a user's understanding of how the system functions is by employing explanations to account for the output produced. There have been attempts to explain intelligent systems over the past three decades. However, each attempt has had shortcomings that separated the logic used to produce the output and that used to produce the explanation. By using the representational paradigm of Contextual Graphs, it is proposed that explanations can be produced to overcome these shortcomings. Two different temporal forms of explanations are proposed, a pre-explanation and a post-explanation. The pre-explanation is intended to help the user understand the decision making process. The post-explanation is intended to help the user understand how the system arrived at a final decision. Both explanations are intended to help the user gain a greater understanding of the logic used to compute the system's output, and thereby enhance the system's credibility and utility. A prototype system is constructed to be used as a decision support tool in a National Science Foundation research program. The researcher has spent the last year at the NSF collecting the knowledge implemented in the prototype system.
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Date Issued: 2005
Identifier: CFE0000713, ucf:46601
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000713

Title: IMITATING INDIVIDUALIZED FACIAL EXPRESSIONS IN A HUMAN-LIKE AVATAR THROUGH A HYBRID PARTICLE SWARM OPTIMIZATION - TABU SEARCH ALGORITHM.
Creator: Husk, Evan, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This thesis describes a machine learning method for automatically imitating a particular person's facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen's Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid...
Show moreThis thesis describes a machine learning method for automatically imitating a particular person's facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen's Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Kennedy and Eberhart's Particle Swarm Optimization algorithm (PSO) and Glover's Tabu Search (TS). Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work.
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Date Issued: 2012
Identifier: CFH0004286, ucf:44949
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004286

Title: CONTEXT-DRIVEN AGENTS IN COMPUTER SUPPORTED COOPERATIVE WORKS.
Creator: Lichtman, Brian, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This thesis describes a research project that investigates the level of contextualization needed to successfully build context-driven agents that can manage a cooperative project. Many times in industry, collaborators in a large project may be located vast distances from each other. It is for this reason that management of such projects can often be difficult. The purpose of this research is to design an agent that can take on the role of a project manager (PM) to assist the human project...
Show moreThis thesis describes a research project that investigates the level of contextualization needed to successfully build context-driven agents that can manage a cooperative project. Many times in industry, collaborators in a large project may be located vast distances from each other. It is for this reason that management of such projects can often be difficult. The purpose of this research is to design an agent that can take on the role of a project manager (PM) to assist the human project manager. Specifically, this thesis looks to give such project management agents full situational awareness. It is hypothesized that only with situational awareness can an agent successfully act in the role of a project manager. This thesis describes the investigation into the use of Context-Based Reasoning and Contextual Graphs to create an agent with such situational awareness. This thesis shows that with enough situational awareness, an agent will have the ability to successfully take on the role of a project manager. In particular, this thesis looks at a PM-agent that can manage a simulated project to design and construct a small sounding rocket.
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Date Issued: 2011
Identifier: CFH0004113, ucf:44871
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004113

Title: ROBUST DIALOG MANAGEMENT THROUGH A CONTEXT-CENTRIC ARCHITECTURE.
Creator: Hung, Victor, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user's goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an...
Show moreThis dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user's goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine's ability to communicate may be hindered by poor reception of utterances, caused by a user's inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by Context-Based Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user's assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users.
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Date Issued: 2010
Identifier: CFE0003230, ucf:48556
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003230

Title: A CONTEXTUAL APPROACH TO LEARNING COLLABORATIVE BEHAVIOR VIA OBSERVATION.
Creator: Johnson, Cynthia, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate...
Show moreThis dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other software agents and creating a behavior function for a single agent. The research described here combines current research in teamwork simulations and learning by observation to effectively train a multi-agent system in effective team behavior. The dissertation describes the background and work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described.
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Date Issued: 2011
Identifier: CFE0003602, ucf:48869
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003602

Title: INCREMENTAL LIFECYCLE VALIDATION OF KNOWLEDGE-BASED SYSTEMS THROUGH COMMONKADS.
Creator: Batarseh, Feras, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This dissertation introduces a novel validation method for knowledge-based systems (KBS).Validation is an essential phase in the development lifecycle of knowledge-based systems. Validation ensures that the system is valid, reliable and that it reflects the knowledge of the expert and meets the specifications. Although many validation methods have been introduced for knowledge-based systems, there is still a need for an incremental validation method based on a lifecycle model. Lifecycle...
Show moreThis dissertation introduces a novel validation method for knowledge-based systems (KBS).Validation is an essential phase in the development lifecycle of knowledge-based systems. Validation ensures that the system is valid, reliable and that it reflects the knowledge of the expert and meets the specifications. Although many validation methods have been introduced for knowledge-based systems, there is still a need for an incremental validation method based on a lifecycle model. Lifecycle models provide a general framework for the developer and a mapping technique from the system into the validation process. They support reusability, modularity and offer guidelines for knowledge engineers to achieve high quality systems. CommonKADS is a set of models that helps to represent and analyze knowledge-based systems. It offers a de facto standard for building knowledge-based systems. Additionally, CommonKADS is a knowledge representation-independent model. It has powerful models that can represent many domains. Defining an incremental validation method based on a conceptual lifecycle model (such as CommonKADS) has a number of advantages such as reducing time and effort, ease of implementation when having a template to follow, well-structured design, and better tracking of errors when they occur. Moreover, the validation method introduced in this dissertation is based on case testing and selecting an appropriate set of test cases to validate the system. The validation method defined makes use of results of prior test cases in an incremental validation procedure. This facilitates defining a minimal set of test cases that provides complete and effective system coverage. CommonKADS doesn't define validation, verification or testing in any of its models. This research seeks to establish a direct relation between validation and lifecycle models, and introduces a validation method for KBS embedded into CommonKADS.
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Date Issued: 2011
Identifier: CFE0003621, ucf:48879
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003621

Title: EPISODIC MEMORY MODEL FOR EMBODIED CONVERSATIONAL AGENTS.
Creator: Elvir, Miguel, Gonzalez, Avelino, University of Central Florida
Abstract / Description: Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into...
Show moreEmbodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECAÃ‚Â's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.
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Date Issued: 2010
Identifier: CFE0003353, ucf:48443
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003353

Title: FALCONET: FORCE-FEEDBACK APPROACH FOR LEARNING FROM COACHING AND OBSERVATION USING NATURAL AND EXPERIENTIAL TRAINING.
Creator: Stein, Gary, Gonzalez, Avelino, University of Central Florida
Abstract / Description: Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine...
Show moreBuilding an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely observable information to construct an agent that behaves similarly to the observed human. Typically, an observational system builds an agent only based on prerecorded observations. This type of system works well with respect to agent creation, but lacks the ability to be trained and updated on-line. To overcome these deficiencies, the proposed system works by adding an augmented force-feedback system of training that senses the agents intentions haptically. Furthermore, because not all possible situations can be observed or directly trained, a third stage of learning from practice is added for the agent to gain additional knowledge for a particular mission. These stages of learning mimic the natural way a human might learn a task by first watching the task being performed, then being coached to improve, and finally practicing to self improve. The hypothesis is that a system that is initially trained using human recorded data (Observational), then tuned and adjusted using force-feedback (Instructional), and then allowed to perform the task in different situations (Experiential) will be better than any individual step or combination of steps.
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Date Issued: 2009
Identifier: CFE0002746, ucf:48157
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002746

Title: CONTEXTUALIZING OBSERVATIONAL DATA FOR MODELING HUMAN PERFORMANCE.
Creator: Trinh, Viet, Gonzalez, Avelino, University of Central Florida
Abstract / Description: This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when...
Show moreThis research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund's research on using the Genetic Context Learner (GenCL) to model CxBR agents from observed human performance [Fernlund, 2004]. To accomplish the process of context discovery, this research proposes two contextualization algorithms: Contextualized Fuzzy ART (CFA) and Context Partitioning and Clustering (COPAC). The former is a more naive approach utilizing the well known Fuzzy ART strategy while the latter is a robust algorithm developed on the principles of CxBR. Using Fernlund's original five drivers, the CFA and COPAC algorithms were tested and evaluated on their ability to effectively contextualize each driver's individualized set of behaviors into well-formed and meaningful context bases as well as generating high-fidelity agents through the integration with Fernlund's GenCL algorithm. The resultant set of agents was able to capture and generalized each driver's individualized behaviors.
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Date Issued: 2009
Identifier: CFE0002563, ucf:48253
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002563

Title: Necessary Conditions for Open-Ended Evolution.
Creator: Soros, Lisa, Stanley, Kenneth, Gonzalez, Avelino, Wiegand, Rudolf, Cash, Mason, University of Central Florida
Abstract / Description: Evolution on Earth is widely considered to be an effectively endless process. Though this phenomenon of open-ended evolution (OEE) has been a topic of interest in the artificial life communitysince its beginnings, the field still lacks an empirically validated theory of what exactly is necessary to reproduce the phenomenon in general (including in domains quite unlike Earth). Thisdissertation (1) enumerates a set of conditions hypothesized to be necessary for OEE in addition to (2)...
Show moreEvolution on Earth is widely considered to be an effectively endless process. Though this phenomenon of open-ended evolution (OEE) has been a topic of interest in the artificial life communitysince its beginnings, the field still lacks an empirically validated theory of what exactly is necessary to reproduce the phenomenon in general (including in domains quite unlike Earth). Thisdissertation (1) enumerates a set of conditions hypothesized to be necessary for OEE in addition to (2) introducing an artificial life world called Chromaria that incorporates each of the hypothesizednecessary conditions. It then (3) describes a set of experiments with Chromaria designed to empirically validate the hypothesized necessary conditions. Thus, this dissertation describes the firstscientific endeavor to systematically test an OEE framework in an alife world and thereby make progress towards solving an open question not just for evolutionary computation and artificial life,but for science in general.
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Date Issued: 2018
Identifier: CFE0007247, ucf:52205
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007247

Artificial Intelligence (5)	+ -
Machine Learning (5)	+ -
Genetic Programming (3)	+ -
NEAT (3)	+ -
artificial intelligence (3)	+ -

machine learning (3)	+ -
Academic (2)	+ -
Academic -- Engineering (2)	+ -
Context (2)	+ -
Context-based Reasoning (2)	+ -
Contexts (2)	+ -
CxBR (2)	+ -
Dissertations (2)	+ -
Engineering -- Dissertations (2)	+ -
Intelligent Tutoring Systems (2)	+ -
Neural Networks (2)	+ -
PSO (2)	+ -
artificial life (2)	+ -
cooperative systems (2)	+ -
neuroevolution (2)	+ -
open-ended evolution (2)	+ -
AI (1)	+ -
Affective (1)	+ -
Android (1)	+ -
Bayesian machine learning (1)	+ -
CSCW (1)	+ -
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ETD Collection (32)	+ -
Honors Collection (2)	+ -

Gonzalez, Avelino (32)	+ -
University of Central Florida (32)	+ -
Sukthankar, Gita (5)	+ -
Stanley, Kenneth (4)	+ -
Wiegand, Rudolf (4)	+ -

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Gonzalez, Avelino J. (2)	+ -
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Fiore, Stephen (1)	+ -
Folsom-Kovarik, Jeremiah (1)	+ -

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