1,721,008 research outputs found
Smart Pathfinding
No full textSummary of the work on "Smart Pathfinding". Two different ways to expand videogame path planning with deliberative capabilities such as solving limited planning problems or taking into account the lack of knowledge on the state of the map
Belief-Driven Pathfinding through personalized map abstraction
No full textWe investigate the case of belief-driven pathfinding (BDP) according to which characters hold a personalized account of a dynamic changing game-world. BDP is concerned with maintaining and revising a set of beliefs that persists over time as a character navigates to subsequent target destinations. This allows for a differentiation among characters with different observations in the game and can provide better believability. We present BGCA∗, a practical BDP approach that is based on (i) decomposing the map into regions, (ii) using personalized beliefs per character about the connectivity of regions, and (iii) employing a regular pathfinding component as a service. We evaluate BGCA∗ in terms of computational effort and precision wrt a regular solver over several benchmark maps. Our results motivate a simple belief revision strategy that induces small overhead and amortizes effort spent toward precision
A Reasoning Module for Long-Lived Cognitive Agents
No full textIn this thesis we study a reasoning module for agents that have cognitive abilities, such as memory, perception, action, and are expected to function autonomously for long periods of time. The module provides the ability to reason about action and change using the language of the situation calculus and variants of the basic action theories. The main focus of this thesis is on the logical problem of progressing an action theory. First, we investigate the conjecture by Lin and Reiter that a practical first-order definition of progression is not appropriate for the general case. We show that Lin and Reiter were indeed correct in their intuitions by providing a proof for the conjecture, thus resolving the open question about the first-order definability of progression and justifying the need for a second-order definition.Then we proceed to identify three cases where it is possible to obtain a first-order progression with the desired properties: i) we extend earlier work by Lin and Reiter and present a case where we restrict our attention to a practical class of queries that may only quantify over situations in a limited way; ii) we revisit the local-effect assumption of Liu and Levesque that requires that the effects of an action are fixed by the arguments of the action and show that in this case a first-order progression is suitable; iii) we investigate a way that the local-effect assumption can be relaxed and show that when the initial knowledge base is a database of possible closures and the effects of the actions arerange-restricted then a first-order progression is also suitable under a just-in-time assumption.
Finally, we examine a special case of the action theories with range-restricted effects and present an algorithm for computing a finite progression. We prove the correctness and the complexity of the algorithm, and show its application in a simple example that is inspired by video games
Story Generation in PDDL Using Character Moods: A Case Study on Iliad's First Book
No full textIn this paper we look into a simple approach for generating character-based stories using planning and the language of PDDL. A story often involves modalities over properties and objects, such as what the characters believe, desire, request, etc. We look into a practical approach that reifies such modalities into normal objects of the planning domain, and relies on a "mood" predicate to represent the disposition of characters based on these objects. A short story is then generated by specifying a goal for the planning problem expressed in terms of the moods of the characters of the story. As a case study of how such a domain for story generation is modeled, we investigate the story of the first book of Homer's Iliad as a solution of an appropriate PDDL domain and problem description
Transforming Situation Calculus Action Theories for Optimised Reasoning
No full textAmong the most frequent reasoning tasks in the situation calculus are projection queries that query the truth of conditions in a future state of affairs. However, in long running action sequences solving the projection problem is complex. The main contribution of this work is a new technique which allows the length of the action sequences to be reduced by reordering independent actions and removing dominated actions; maintaining semantic equivalence with respect to the original action theory. This transformation allows for the removal of actions that are problematic with respect to progression, allowing for periodical update of the action theory to reflect the current state of affairs. We provide the logical framework for the general case and give specific methods for two important classes of action theories. The work provides the basis for handling more expressive cases, such as the reordering of sensing actions in order to delay progression, and forms an important step towards facilitating ongoing planning and reasoning by long-running agents. It provides a mechanism for minimising the need for keeping the action history while appealing to both regression and progression. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
Action-Based Imperative Programming with YAGI
No full textMany tasks for autonomous agents or robots are best de- scribed by a specification of the environment and a specification of the available actions the agent or robot can perform. Combining such a specification with the possibility to imper- atively program a robot or agent is what we call the action- based imperative programming. One of the most successful such approaches is Golog. In this paper, we draft a proposal for a new robot programming language YAGI, which is based on the action-based imperative programming paradigm. Our goal is to design a small, portable stand-alone YAGI interpreter. We combine the benefits of a principled domain specification with a clean, small and simple programming language, which does not exploit any side-effects from the implementation language. We discuss general requirements of action-based programming languages and outline YAGI, our action-based language approach which particularly aims at embeddability
The Wumpus World in INDIGOLOG: A Preliminary Report
No full textThis paper describes an implementation of the the Wumpus World [Russell and Norving, 2003] in INDIGOLOG with the objective of showing the applicability of this interleaved agent programming language for modeling agent behavior in realistic domains. We briefly go over the INDIGOLOG architecture, explain how we can reason about the Wumpus World domain, and show how to express agent behavior using high-level agent programs. Finally, we discuss initial empirical results obtained as well as challenging issues to be resolved.
Benchmarking smart spaces through autonomous virtual agents
No full textIn the recent years there has been a growing interest in the design and implementation of smart homes. The evaluation of these approaches requires massive datasets of measurements from deployed sensors in real prototypes. While datasets obtained by real smart homes are freely available, they are not sufficient for comparing different approaches and techniques in a variety of configurations. In this work we propose a smart home dataset generation strategy based on a simulated environment populated with virtual autonomous agents, sensors and devices that allow to customize and reproduce a smart space using series of useful parameters. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
Synthesizing daily life logs through gaming and simulation
No full textIn the recent years there has been a growing interest in the design and implementation of smart homes, and smart buildings in general. The evaluation of approaches in this area typically requires massive datasets of measurements from deployed sensors in real prototypes. While a few datasets obtained by real smart homes are freely available, they are not sufficient for comparing different approaches and techniques in a variety of configurations. In this work, we propose a smart home dataset generation strategy based on a simulated environment populated with virtual autonomous agents, sensors and devices which allow to customize and reproduce a smart space using a series of useful parameters. The simulation is based on declarative process models for modeling habits performed by agents, an action theory for realizing low-level atomic actions, and a 3D virtual execution environment. We show how different configurations generate a variety of sensory logs that can be used as input to a state-of-the-art activity recognition technique in order to evaluate its performance under parametrized scenarios, as well as provide guidelines for actually building real smart homes. Copyright © 2013 ACM
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