In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. These advantages become important as more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation. I discuss the design and properties of several agent-centered search methods, focusing on robot exploration and localization.

The field of adaptive robotics involves simulations and real-world implementations of robots that adapt to their environments. In this article, I introduce adaptive environmentics -- the flip side of adaptive robotics -- in which the environment adapts to the robot. To illustrate the approach, I offer three simple experiments in which a genetic algorithm is used to shape an environment for a simulated khepera robot. I then discuss at length the potential of adaptive environmentics, also delineating several possible avenues of future research.

How do we decide how to represent an intelligent system in its interface, and how do we decide how the interface represents information about the world and about its own workings to a user? This article addresses these questions by examining the interaction between representation and intelligence in user interfaces. The rubric representation covers at least three topics in this context: (1) how a computational system is represented in its user interface, (2) how the interface conveys its representations of information and the world to human users, and (3) how the system's internal representation affects the human user's interaction with the system. I argue that each of these kinds of representation (of the system, information and the world, the interaction) is key to how users make the kind of attributions of intelligence that facilitate their interactions with intelligent systems. In this vein, it makes sense to represent a systmem as a human in those cases where social collaborative behavior is key and for the system to represent its knowledge to humans in multiple ways on multiple modalities. I demonstrate these claims by discussing issues of representation and intelligence in an embodied conversational agent -- an interface in which the system is represented as a person, information is conveyed to human users by multiple modalities such as voice and hand gestures, and the internal representation is modality independent and both propositional and nonpropositional.

Choosing an environment is an important decision for agent developers. A key issue in this decision is whether the environment will provide realistic problems for the agent to solve, in the sense that the problems are true to the issues that arise in addressing a particular research question. In addition to realism, other important issues include how tractable problems are that can be formulated in the environment, how easy agent performance can be measured, and whether the environment can be customized or extended for specific research questions. In the ideal environment, researchers can pose realistic but tractable problems to an agent, measure and evaluate its performance, and iteratively rework the environment to explore increasingly ambitious questions, all at a reasonable cost in time and effort. As might be expected, trade-offs dominate the suitability of an environment; however, we have found that the modern graphic user interface offers a good balance among these trade-offs. This article takes a brief tour of agent research in the user interface, showing how significant questions related to vision, planning, learning, cognition, and communication are currently being addressed.

Lifelike characters, or animated agents, provide a promising option for interface development because they allow us to draw on communication and interaction styles with which humans are already familiar. In this contribution, we revisit some of our past and ongoing projects to motivate an evolution of character-based presentation systems. This evolution starts from systems in which a character presents information content in the style of a TV presenter. It moves on with the introduction of presentation teams that convey information to the user by performing role plays. To explore new forms of active user involvement during a presentation, the next step can lead to systems that convey information in the style of interactive performances. From a technical point of view, this evaluation is mirrored in different approaches to determine the behavior of the employed characters. By means of concrete applications, we argue that a central planning component for automated agent scripting is not always a good choice, especially not in the case of interactive performances where the user might take on an active role as well.

C. Furniture, Fixtures and Equipment: Effective for 1996 the Association Furniture, fixtures and equipment are has changed its method of accounting stated at cost, less accumulated depreciation.

We briefly describe the implementation and evaluation of a novel plan synthesis system, called AltAlt. AltAlt is designed to exploit the complementary strengths of two of the currently popular competing approaches for plan generation: (1) graphplan and (2) heuristic state search. It uses the planning graph to derive effective heuristics that are then used to guide heuristic state search. The heuristics derived from the planning graph do a better job of taking the subgoal interactions into account and, as such, are significantly more effective than existing heuristics. AltAlt was implemented on top of two state-of-the-art planning systems: (1) stan3.0, a graphplan-style planner, and (2) hsp-r, a heuristic search planner.

Creativity is sometimes taken to be an inexplicable aspect of human activity. By summarizing a considerable body of literature on creativity, I hope to show how to turn some of the best ideas about creativity into programs that are demonstrably more creative than any we have seen to date. I believe the key to building more creative programs is to give them the ability to reflect on and modify their own frameworks and criteria. That is, I believe that the key to creativity is at the metalevel.

TALplanner is a forward-chaining planner that utilizes domain-dependent knowledge to control search in the state space generated by action invocation. The domain-dependent control knowledge, background knowledge, plans, and goals are all represented using formulas in a temporal logic called tal, which has been developed independently as a formalism for specifying agent narratives and reasoning about them. In the Fifth International Artificial Intelligence Planning and Scheduling Conference planning competition, TALplanner exhibited impressive performance, winning the Outstanding Performance Award in the Domain-Dependent Planning Competition. In this article, we provide an overview of TALplanner

For more details, see Nau et al. 's preconditions can include logical inferences, 's preconditions two methods for traveling from one location can include Horn-clause inferencing, numeric to another: (1) traveling by airplane and (2) computations, and calls to external programs. 's expressive power can be used to create a totally ordered list of subtasks. Suppose domain representations for complex application that all these subtasks are primitive except for domains. For example, the Horn 4. if t is primitive (i.e., there is an operator for t) then clauses can include calls to attached procedures 5. nondeterministically choose an operator o for t We believe the primary 14. endif's higher level of expressivity made it possible to formulate highly expressive domain algorithms in's data structures to make them faster; for example, we found that a simple change to the data structure We intend to make more optimizations in the near future. (Aha and Breslow 1997).