Conventional processor architectures are restricted in exploiting instruction level parallelism (ILP) due to the relatively low number of programmer-visible registers. Therefore, more recent processor architectures ex...
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Conventional processor architectures are restricted in exploiting instruction level parallelism (ILP) due to the relatively low number of programmer-visible registers. Therefore, more recent processor architectures expose their datapaths so that the compiler (1) can schedule parallel instructions to different processing units and (2) can make effective use of local storage of the processing units. Among these architectures, the Synchronous Control Asynchronous Dataflow (SCAD) architecture is a new exposed datapath architecture whose processing units are equipped with first-in first-out (FIFO) buffers at their input and output ports. In contrast to register-based machines, the optimal code generation for SCAD is still a matter of research. In particular, SAT and SMT solvers were used to generate optimal resource constrained and optimal time constrained schedules for SCAD, respectively. As answer set programming (ASP) offers better flexibility in handling such scheduling problems, we focus in this paper on using an answerset solver for both resource and time constrained optimal SCAD code generation. As a major benefit of using ASP, we are able to generate all optimal schedules for a given program which allows one to study their properties. Furthermore, the experimental results of this paper demonstrate that the answerset solver can compete with SAT solvers and outperforms SMT solvers. This paper is under consideration for acceptance in TPLP.
We present the AutoConcierge system that can "understand" human dialogs in a specific domain, namely, restaurant recommendation. AutoConcierge will interactively "understand" a user's utterance...
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ISBN:
(纸本)9783031520372;9783031520389
We present the AutoConcierge system that can "understand" human dialogs in a specific domain, namely, restaurant recommendation. AutoConcierge will interactively "understand" a user's utterances, and request the user to provide required information via a natural language reply. AutoConcierge uses GPT-3 to convert human dialogs into predicates that represent knowledge implicit in the dialogs. These predicates are then input into the goal-directed s(CASP) answer set programming (ASP) system for performing commonsense reasoning to compute responses in the form of predicates. GPT-3 is used again to convert these computed predicates into natural language sentences that are communicated to the user. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human based on truly understanding user utterances. The framework used for AutoConcierge provides a recipe for developing other task-specific chatbots leveraging large language models and answer set programming.
We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots bet...
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We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes);2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We present a mathematical definition of this problem and analyze its computational complexity. We introduce a novel, logic-based method to solve this problem, utilizing action languages and answer set programming for representation, and the state-of-the-art ASP solvers for reasoning. We show the applicability and usefulness of our approach by experiments on various scenarios of responsive and energy-efficient cognitive factories.
In this paper we introduce preference rules which allow us to specify preferences as an ordering among the possible solutions of a problem. Our approach allow us to express preferences for general theories. The formal...
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ISBN:
(纸本)9783540490265
In this paper we introduce preference rules which allow us to specify preferences as an ordering among the possible solutions of a problem. Our approach allow us to express preferences for general theories. The formalism used to develop our work is answer set programming. Two distinct semantics for preference logic programs are proposed. Finally, some properties that help us to understand these semantics are also presented.
In the wider perspective of narrowing down some of the gaps that prevent the adoption of declarative logic programming within highly dynamically changing environments, we focus in this paper on the context of integrat...
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ISBN:
(纸本)9783030351663;9783030351656
In the wider perspective of narrowing down some of the gaps that prevent the adoption of declarative logic programming within highly dynamically changing environments, we focus in this paper on the context of integrating reasoning modules in real-time videogames. Integrating rule-based AI within the commercial game development life-cycle poses a number of unsolved challenges, each with non-obvious solution. For instance, it is necessary to cope with strict time performance requirements;the duality between procedural code and declarative specifications prevents easy integration;the concurrent execution of reasoning tasks and game updates requires proper information passing strategies between the two involved sides. In this work we illustrate our recent progress on how to embed rule-based reasoning modules into the well-known Unity game development engine. To this end, we report about ThinkEngine, a framework in which a tight integration of declarative formalisms within the typical game development workflow is made possible. We prove the viability of our approach by developing a proof-of-concept Unity game that makes use of ASP-based AI modules.
In contrast to simple autonomous vacuum cleaners, multi-purpose robots that fetch a cup of coffee and clean up rooms require cognitive skills such as learning, planning, and reasoning. Especially reasoning in dynamic ...
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ISBN:
(纸本)9789897582202
In contrast to simple autonomous vacuum cleaners, multi-purpose robots that fetch a cup of coffee and clean up rooms require cognitive skills such as learning, planning, and reasoning. Especially reasoning in dynamic and human populated environments demands for novel approaches that can handle comprehensive and fluent knowledge bases. A promising approach is answer set programming (ASP), offering multi-shot solving techniques and non-monotonic stable model semantics. Our objective is to equip multi-agent systems with ASP-based reasoning capabilities, enabling a team of robots to cope with dynamic environments. Therefore, we combined ALICA - A Language for Interactive Cooperative Agents - with the ASP solver Clingo and chose topological path planning as our evaluation scenario. We utilised the Region Connection Calculus as underlying formalism of our evaluation and investigated the scalability of our implementation. The results show that our approach handles dynamic environments and scales up to appropriately large problem sizes.
Epistemic specification (ES for short) is an extension of answer set programming (ASP for short). The extension is built around the introduction of modalities K and M, and then is capable of representing incomplete in...
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ISBN:
(纸本)9781479929719
Epistemic specification (ES for short) is an extension of answer set programming (ASP for short). The extension is built around the introduction of modalities K and M, and then is capable of representing incomplete information in the presence of multiple belief sets. Although both syntax and semantics of ES are up in the air, the need for this extension has been illustrated with several examples in the literatures. In this paper, we present a new ES version with only modality K and the design of its inference engine ESmodels that aims to be efficient enough to promote the theoretical research and also practical use of ES. We first introduce the syntax and semantics of the new version of ES and show it is succinct but flexible by comparing it with existing ES versions. Then, we focus on the description of the algorithm and optimization approaches of the inference engine. Finally, we conclude with perspectives.
In the latest years, Stream Reasoning (SR) has become increasingly relevant in various scenarios where it is required to reason over heterogeneous and highly dynamic data streams, typically along with large background...
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ISBN:
(纸本)9798400709692
In the latest years, Stream Reasoning (SR) has become increasingly relevant in various scenarios where it is required to reason over heterogeneous and highly dynamic data streams, typically along with large background knowledge bases, such as Smart Cities, IoT, Healthcare, etc. In this context, several solutions based on answer set programming (ASP) have been successfully employed. Nevertheless, real applications showed that it is often needed to deal with events over the timeline generating specific patterns that, in turn, can fire additional events or invalidate others. In this respect, current ASP-based state of the art systems appear not fully satisfactory, both from a modelling point of view and when it comes to usability and performance. In this work, starting from a well-established ASP-based SR solution, namely I-DLV-sr, we: (i) extend the language with means to explicitly define, identify and reason about patterns of events and their consequences, possibly spanning across the timeline;(ii) generalize the system architecture so that it is able to decouple language and implementation support from the choice of a specific ASP system, thus allowing the user to select the one best suited to the specific programming framework for Stream Reasoning. DP-sr is put to the test, showing both the ease in modelling and performance improvements.
Game-based learning environments hold significant potential for supporting K-12 computer science (CS) education by providing CS learning experiences embedded within engaging virtual worlds. However, many gamebased lea...
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ISBN:
(纸本)9783030522407;9783030522391
Game-based learning environments hold significant potential for supporting K-12 computer science (CS) education by providing CS learning experiences embedded within engaging virtual worlds. However, many gamebased learning environments do not adaptively support individual students based on their specific knowledge and skills. Often, this is because creating game levels is highly labor-intensive, which limits the number of levels created to support student learning. Procedural content generation (PCG) is a promising direction for addressing this challenge by dynamically creating game levels that address specific student needs without requiring extensive development effort. In this paper, we investigate a PCG framework driven by answer set programming (ASP), a variant of logic programming that utilizes well-formed logical rules to express constraints for valid game levels. We demonstrate how variations in CS learning objectives and game-playing skills can be incorporated into ASP-based rules to generate learner-adaptive levels in a middle-grades CS game-based learning environment. Evaluations of the generated levels suggest that the ASP-based level generator not only reliably generates desired CS educational game levels but also synthesizes a large set of diverse game levels. The findings suggest that the ASP-based PCG approach has considerable promise for creating highly engaging and adaptive game-based learning experiences for K-12 CS education.
There is an increasing interest in using logic programming to specify and implement distributed algorithms, including a variety of network applications. These are applications where data and computation are distribute...
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There is an increasing interest in using logic programming to specify and implement distributed algorithms, including a variety of network applications. These are applications where data and computation are distributed among several devices and where, in principle, all the devices can exchange data and share the computational results of the group. In this paper we propose a declarative approach to distributed computing whereby distributed algorithms and communication models can be (i) specified as action theories of fluents and actions;(ii) executed as collections of distributed state machines, where devices are abstracted as (input/output) automata that can exchange messages;and (iii) analysed using existing results on connecting causal theories and answer set programming. Results on the application of our approach to different classes of network protocols are also presented.
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