The Behavior Language; User's Guide

The Behavior Language is a rule-based real-time parallel robot programming language originally based on ideas from [Brooks 86], [Connell 89], and [Maes 89]. It compiles into a modified and extended version of the subsumption architecture [Brooks 86] and thus has backends for a number of processors including the Motorola 68000 and 68HCll, the Hitachi 6301, and Common Lisp. Behaviors are groups of rules which are activatable by a number of different schemes. There are no shared data structures across behaviors, but instead all communication is by explicit message passing. All rules are assumed to run in parallel and asynchronously. It includes the earlier notions of inhibition and suppression, along with a number of mechanisms for spreading of activation.

Internet Fish

I have invented "Internet Fish," a novel class of resource-discovery tools designed to help users extract useful information from the Internet. Internet Fish (IFish) are semi-autonomous, persistent information brokers; users deploy individual IFish to gather and refine information related to a particular topic. An IFish will initiate research, continue to discover new sources of information, and keep tabs on new developments in that topic. As part of the information-gathering process the user interacts with his IFish to find out what it has learned, answer questions it has posed, and make suggestions for guidance. Internet Fish differ from other Internet resource discovery systems in that they are persistent, personal and dynamic. As part of the information-gathering process IFish conduct extended, long-term conversations with users as they explore. They incorporate deep structural knowledge of the organization and services of the net, and are also capable of on-the-fly reconfiguration, modification and expansion. Human users may dynamically change the IFish in response to changes in the environment, or IFish may initiate such changes itself. IFish maintain internal state, including models of its own structure, behavior, information environment and its user; these models permit an IFish to perform meta-level reasoning about its own structure. To facilitate rapid assembly of particular IFish I have created the Internet Fish Construction Kit. This system provides enabling technology for the entire class of Internet Fish tools; it facilitates both creation of new IFish as well as additions of new capabilities to existing ones. The Construction Kit includes a collection of encapsulated heuristic knowledge modules that may be combined in mix-and-match fashion to create a particular IFish; interfaces to new services written with the Construction Kit may be immediately added to "live" IFish. Using the Construction Kit I have created a demonstration IFish specialized for finding World-Wide Web documents related to a given group of documents. This "Finder" IFish includes heuristics that describe how to interact with the Web in general, explain how to take advantage of various public indexes and classification schemes, and provide a method for discovering similarity relationships among documents.

Automatic Qualitative Modeling of Dynamic Physical Systems

This report describes MM, a computer program that can model a variety of mechanical and fluid systems. Given a system's structure and qualitative behavior, MM searches for models using an energy-based modeling framework. MM uses general facts about physical systems to relate behavioral and model properties. These facts enable a more focussed search for models than would be obtained by mere comparison of desired and predicted behaviors. When these facts do not apply, MM uses behavior-constrained qualitative simulation to verify candidate models efficiently. MM can also design experiments to distinguish among multiple candidate models.

Understanding and Modeling the Behavior of a Harmonic Drive Gear Transmission

In my research, I have performed an extensive experimental investigation of harmonic-drive properties such as stiffness, friction, and kinematic error. From my experimental results, I have found that these properties can be sharply non-linear and highly dependent on operating conditions. Due to the complex interaction of these poorly behaved transmission properties, dynamic response measurements showed surprisingly agitated behavior, especially around system resonance. Theoretical models developed to mimic the observed response illustrated that non-linear frictional effects cannot be ignored in any accurate harmonic-drive representation. Additionally, if behavior around system resonance must be replicated, kinematic error and transmission compliance as well as frictional dissipation from gear-tooth rubbing must all be incorporated into the model.

Modeling Robot Dynamic Performance for Endpoint Force Control

This research aims to understand the fundamental dynamic behavior of servo-controlled machinery in response to various types of sensory feedback. As an example of such a system, we study robot force control, a scheme which promises to greatly expand the capabilities of industrial robots by allowing manipulators to interact with uncertain and dynamic tasks. Dynamic models are developed which allow the effects of actuator dynamics, structural flexibility, and workpiece interaction to be explored in the frequency and time domains. The models are used first to explain the causes of robot force control instability, and then to find methods of improving this performance.

Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services

Many online services access a large number of autonomous data sources and at the same time need to meet different user requirements. It is essential for these services to achieve semantic interoperability among these information exchange entities. In the presence of an increasing number of proprietary business processes, heterogeneous data standards, and diverse user requirements, it is critical that the services are implemented using adaptable, extensible, and scalable technology. The COntext INterchange (COIN) approach, inspired by similar goals of the Semantic Web, provides a robust solution. In this paper, we describe how COIN can be used to implement dynamic online services where semantic differences are reconciled on the fly. We show that COIN is flexible and scalable by comparing it with several conventional approaches. With a given ontology, the number of conversions in COIN is quadratic to the semantic aspect that has the largest number of distinctions. These semantic aspects are modeled as modifiers in a conceptual ontology; in most cases the number of conversions is linear with the number of modifiers, which is significantly smaller than traditional hard-wiring middleware approach where the number of conversion programs is quadratic to the number of sources and data receivers. In the example scenario in the paper, the COIN approach needs only 5 conversions to be defined while traditional approaches require 20,000 to 100 million. COIN achieves this scalability by automatically composing all the comprehensive conversions from a small number of declaratively defined sub-conversions.

Association Behavior of Biotinylated and Non-Biotinylated PolyEthylene Oxide-b-Poly(2-(Diethylamino)Ethyl Methacrylate)

Biotinylated and non-biotinylated copolymers of ethylene oxide (EO) and 2-(diethylamino)ethyl methacrylate (DEAEMA) were synthesized by the atom transfer radical polymerization technique (ATRP). The chemical compositions of the copolymers as determined by NMR are represented by PEO₁₁₃PDEAEMA₇₀ and biotin-PEO₁₀₄PDEAEMA₉₃ respectively. The aggregation behavior of these polymers in aqueous solutions at different pHs and ionic strengths was studied using a combination of potentiometric titration, dynamic light scattering (DLS), static light scattering (SLS), and transmission electron microscopy (TEM). Both PEO-b-PDEAEMA and biotin-PEO-b-PDEAEMA diblock copolymers form micelles at high pH with hydrodynamic radii (Rh) of about 19 and 23 nm, respectively. At low pH, the copolymers are dispersed as unimers in solution with Rh of about 6-7 nm. However, at a physiological salt concentration (cs) of about 0.16M NaCl and a pH of 7-8, the copolymers form large loosely packed Guassian chains, which were not present at the low cs of 0.001M NaCl. The critical micelle concentrations (CMC) and the cytotoxicity of the copolymers were investigated to determine a suitable polymer concentration range for future biological applications. Both PEO-b-PDEAEMA and biotin-PEO-b-PDEAEMA diblock copolymers possess identical CMC values of about 0.0023 mg/g, while the cytotoxicity test indicated that the copolymers are not toxic up to 0.05mg/g (> 83% cell survival at this concentration).

Synthesis and Aggregation Behavior of Pluronic F87/Poly(acrylic acid) Block Copolymer with Doxorubicin

Poly(acrylic acid) (PAA) was grafted onto both termini of Pluronic F87 (PEO₆₇-PPO₃₉-PEO₆₇) via atom transfer radical polymerization to produce a novel muco-adhesive block copolymer PAA₈₀-b-F₈₇-b-PAA₈₀. It was observed that PAA₈₀-F₈₇-PAA₈₀ forms stable complexes with weakly basic anti-cancer drug, Doxorubicin. Thermodynamic changes due to the drug binding to the copolymer were assessed at different pH by isothermal titration calorimetry (ITC). The formation of the polymer/drug complexes was studied by turbidimetric titration and dynamic light scattering. Doxorubicin and PAA-b-F87-b-PAA block copolymer are found to interact strongly in aqueous solution via non-covalent interactions over a wide pH range. At pH>4.35, drug binding is due to electrostatic interactions. Hydrogen-bond also plays a role in the stabilization of the PAA₈₀-F₈₇-PAA₈₀/DOX complex. At pH 7.4 (α=0.8), the size and stability of polymer/drug complex depend strongly on the doxorubicin concentration. When CDOX <0.13mM, the PAA₈₀-F₈₇-PAA₈₀ copolymer forms stable inter-chain complexes with DOX (110 ~ 150 nm). When CDOX >0.13mM, as suggested by the light scattering result, the reorganization of the polymer/drug complex is believed to occur. With further addition of DOX (CDOX >0.34mM), sharp increase in the turbidity indicates the formation of large aggregates, followed by phase separation. The onset of a sharp enthalpy increase corresponds to the formation of a stoichiometric complex.