Creating artificial intelligent agents that are high-fidelity simulations of natural agents will require the engagement of behavioral scientists. However, agent programming systems that are accessible to behavioral scientists are too limited to create rich agents, and systems for creating rich agents are accessible mainly to computer scientists, not behavioral scientists. We are solving this problem by engaging behavioral scientists in the design of a programming language, and integrating reinforcement learning into the programming language. This strategy will help our language achieve adaptivity, modularity, and, most importantly, accessibility to behavioral scientists. In addition to allowing behavioral scientist to write rich agent programs, our language — AFABL (A Friendly Behavior Language) — will enable a true discipline of modular agent software engineering with broad implications for games, interactive storytelling, and social simulations.

This My thesis work combines AI, programming language design, incompleteness of perception and dynamism in the environment and software engineering. I am integrating reinforcement creates a strong need for adaptivity. Programming this learning (RL) into a programming language so adaptivity by hand in a language that does not provide builtin that the language achieves three primary goals: accessibility, support for adaptivity is very cumbersome. As I demonstrated adaptivity, and modularity. If I am successful, my or designer specifies the structure of certain parts work will enable a discipline of modular large-scale agent of a program while leaving other portions unspecified, such software engineering while making advanced agent modeling that a learning system can learn how to perform them.

In recent years Bayesian networks have been used in many fields, from Online Analytical Processing (OLAP) performance enhancement (Margaritis 2003) to medical service performance analysis (Acid et al. 2004), gene expression analysis (Friedman et al. 2000), breast cancer prognosis and epidemiology (Holmes and Jain 2008). The high dimensionality of the data sets common in these domains have led to the development of several learning algorithms focused on reducing computational complexity while still learning the correct network. Some examples are the Grow-Shrink algorithm in Margaritis (2003), the Incremental Association algorithm and its derivatives in Tsamardinos et al. (2003) and in Yaramakala and Margaritis (2005), the Sparse Candidate algorithm in Friedman et al. (1999), the Optimal Reinsertion in Moore and Wong (2003) and the Greedy Equivalent Search in Chickering (2002). The aim of the bnlearn package is to provide a free implementation of some of these structure learning algorithms along with the conditional independence tests and network scores used 2 Learning Bayesian Networks with the bnlearn R Package to construct the Bayesian network. Both discrete and continuous data are supported. Furthermore, the learning algorithms can be chosen separately from the statistical criterion they are based on (which is usually not possible in the reference implementation provided by the algorithms' authors), so that the best combination for the data at hand can be used.

Knowledge compilation is a valuable tool for dealing with the computational intractability of propositional reasoning. In knowledge compilation, a representation in a source language is typically compiled into a target language in order to perform some reasoning task in polynomial time. One particularly popular target language is Deterministic Decomposable Negation Normal Form (d-DNNF). d-DNNF supports efficient reasoning for tasks such as consistency checking and model counting, and as such it has proven a useful representation language for Bayesian inference, conformant planning, and diagnosis. In this paper, we exploit recent advances in #SAT solving in order to produce a new state-of-the-art CNF → d-DNNF compiler. We evaluate the properties and performance of our compiler relative to C2D, the de facto standard for compiling to d-DNNF. Empirical results demonstrate that our compiler is generally one order of magnitude faster than C2D on typical benchmark problems while yielding a d-DNNF representation of comparable size.

To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to best-first heuristic search in a shared-memory setting. Each thread attempts to expand the most promising nodes. By using abstraction to partition the state space, we detect duplicate states while avoiding lock contention. We allow speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8-core machine, we show that A* implemented in our framework yields faster search performance than previous parallel search proposals. We also demonstrate that our approach extends easily to other best-first searches, such as weighted A* and anytime heuristic search.

Early detection of cognitive impairment can prevent or delay the progress of cognitive dysfunction. In the field of neurology, the Clock Drawing Test (CDT) is one of the most popular instruments for detecting cognitive impairment. This paper presents the development of the ClockReader system, a computerized Clock Drawing Test. The main function of the system is to automate error handling in handwriting recognition. Since the ClockReader is a screening tool for dementia, it is not desirable to ask the users to fix their input errors in the drawing of either numbers or characters. Therefore, we propose a simple machine learning technique, context-bounded refinement filter algorithm. With trial experiments, we prove that this simple algorithm improves the recognizer accuracy of handwriting in clock drawings up to 88%.

The overall goal of our research is the automatic recognition of the intended message of a grouped bar chart. This paper presents our preliminary work on a system that utilizes the communicative signals in a grouped bar chart as evidence in a Bayesian network that hypothesizes the primary message conveyed by the graphic. The paper discusses the kinds of communicative signals present in grouped bar charts and an ACT-R model for computationalizing one important communicative signal, the relative effort involved in performing the perceptual tasks necessary for the recognition. It also describes our Bayesian network and its implementation on a subset of the kinds of messages that can be conveyed by grouped bar charts.

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident’s activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents, based on a library of known team-activities (plan library). It has important applications in analyzing data from automated monitoring, surveillance, and intelligence analysis in general. Recently, we have introduced a model for MAPR with a flat library structure, to study the complexity of basic MAPR, and also possibly its extensions in the future. Interestingly, this model makes fewer assumptions than existing models, and hence is more general. Therefore, as no existing algorithm would apply to this model, we have developed an hypothesis generation algorithm for this model, and adapted Knuth's Algorithm X for branch and bound search in the resulting hypothesis space. In this paper, we establish the time complexity of hypothesis generation in this model, propose and evaluate 3 different bounding criteria, and also empirically study the dependence of runtimes (hypothesis generation, and search times separately) on the model parameters.

Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently also to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges: sparseness, ambiguity, noise, and inconsistency. We describe an approach to folksonomy learning based on relational clustering that addresses these challenges by exploiting structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr. We evaluate the learned folksonomy quantitatively by automatically comparing it to a reference taxonomy. Our empirical results suggest that the proposed framework, which addresses the challenges listed above, improves on existing folksonomy learning methods.