An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

COMPETITIVE ADVANTAGE BY INTEGRATED E-BUSINESS IN SUPPLY CHAINS: A STRATEGIC APPROACH

This paper reports findings of the competitive advantage of supply chain integration and the crucial role of integrated e-business to deliver those benefits. However, adoption of e-business in supply chains has been slower than expected, particularly in small to medium sized enterprises (SMEs). Differences between firms in supply chains and between supply chains are examined. Across industries, firms have adopted e-business initiatives to better manage their internal business processes as well as their interfaces with the environment. The authors’ findings provide the foundation for a more rigorous study of e-business.e-business, IT business value, supply chain management, value chain, competitive advantage

Online Build-Order Optimization for Real-Time Strategy Agents Using Multi-Objective Evolutionary Algorithms

The investigation introduces a novel approach for online build-order optimization in real-time strategy (RTS) games. The goal of our research is to develop an artificial intelligence (AI) RTS planning agent for military critical decision- making education with the ability to perform at an expert human level, as well as to assess a players critical decision- making ability or skill-level. Build-order optimization is modeled as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA) that provides a set of good build-orders to a RTS planning agent. We de ne three research objectives: (1) Design, implement and validate a capability to determine the skill-level of a RTS player. (2) Design, implement and validate a strategic planning tool that produces near expert level build-orders which are an ordered sequence of actions a player can issue to achieve a goal, and (3) Integrate the strategic planning tool into our existing RTS agent framework and an RTS game engine. The skill-level metric we selected provides an original and needed method of evaluating a RTS players skill-level during game play. This metric is a high-level description of how quickly a player executes a strategy versus known players executing the same strategy. Our strategic planning tool combines a game simulator and an MOEA to produce a set of diverse and good build-orders for an RTS agent. Through the integration of case-base reasoning (CBR), planning goals are derived and expert build- orders are injected into a MOEA population. The MOEA then produces a diverse and approximate Pareto front that is integrated into our AI RTS agent framework. Thus, the planning tool provides an innovative online approach for strategic planning in RTS games. Experimentation via the Spring Engine Balanced Annihilation game reveals that the strategic planner is able to discover build-orders that are better than an expert scripted agent and thus achieve faster strategy execution times

Progressive Horizon Planning

In an earlier paper [Rymon et a1 89], we showed how domain localities and regularities can be used to reduce the complexity of finding a trauma management plan that satisfies a set of diagnostic and therapeutic goals. Here, we present another planning idea - Progressive Horizon - useful for optimizing such plans in domains where planning can be regarded as an incremental process, continuously interleaved with situation - goals analysis and plan execution. In such domains, planned action cannot be delayed until all essential information is available: A plan must include actions intended to gather information as well as ones intended to change the state of the world. Interleaving planning with reasoning and execution, a progressive horizon planner constructs a plan that answers all currently known needs but has only its first few actions optimized (those within its planning horizon). As the executor cames out actions and reports back to the system, the current goals and the plan are updated based on actual performance and newly discovered goals and information. The new plan is then optimized within a newly set horizon. In this paper, we describe those features of a domain that are salient for the use of a progressive horizon planning paradigm. Since we believe that the paradigm may be useful in other domains, we abstract from the exact techniques used by our program to discuss the merits of the general approach

Knowledge-Based Task Structure Planning for an Information Gathering Agent

An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed