Papers by Sebastian Sardina
We look at composition of (possibly nonterminating) high-level programs over situation calculus a... more We look at composition of (possibly nonterminating) high-level programs over situation calculus action theories. Specifically the problem we look at is as follows: given a library of available ConGolog programs and a target program not in the library, verify whether the target program executions be realized by composing fragments of the executions of the available programs; and, if so, synthesize a controller that does the composition automatically. This kind of composition problems have been investigated in the CS and AI literature, but always assuming finite states settings. Here, instead, we investigate the issue in the context of infinite domains that may go through an infinite number of states as a result of actions. Obviously in this context the problem is undecidable. Nonetheless, by exploiting recent results in the AI literature, we devise a sound and well characterized technique to actually solve the problem.
When it comes to building controllers for robots or agents, high-level programming languages like... more When it comes to building controllers for robots or agents, high-level programming languages like Golog and ConGolog offer a useful compromise between planning-based approaches and low-level robot programming. However, two serious problems typically emerge in practical implementations of these languages: how to evaluate tests in a program efficiently enough in an open-world setting, and how to make appropriate nondeterministic choices while avoiding full lookahead. Recent proposals in the literature suggest that one could tackle the first problem by exploiting sensing information, and tackle the second by specifying the amount of lookahead allowed explicitly in the program. In this paper, we combine these two ideas and demonstrate their power by presenting an interpreter, written in Prolog, for a variant of Golog that is suitable for efficiently operating in open-world setting by exploiting sensing and bounded lookahead.
Lecture Notes in Computer Science, 2015
In Belief Desire Intention (BDI) agent systems it is usual for goals to have a number of plans th... more In Belief Desire Intention (BDI) agent systems it is usual for goals to have a number of plans that are possible ways of achieving the goal, applicable in different situations, usually captured by a context condition. In Agent Oriented Software Engineering it has been suggested that a designer should be conscious of whether a goal has complete coverage, that is, is there some plan that is applicable for every situation. Similarly a designer should be conscious of overlap, that is, for a given goal, are there situations where more than one plan could be applicable for achieving that goal. In this paper we further develop these notions in two ways, and then describe how they can be used both in agent reasoning and agent system development. Firstly we replace the boolean value for basic coverage and overlap with numerical measures, and explain how these may be calculated. Secondly we describe a measure that combines these basic measures, with the characteristics of the coverage/overlap...
Autonomous agents typically have several goals they are pursuing simultaneously. Even if the goal... more Autonomous agents typically have several goals they are pursuing simultaneously. Even if the goals themselves are not necessarily inconsistent, choices made about how to pursue each of these goals may well result in a set of intentions which are conflicting. A rational autonomous agent should be able to reason about and modify its set of intentions to take account of such issues. This paper presents the semantics of some preferences regarding modified sets of intentions. We look at the possibility of simply deleting some intention(s) but more importantly we also look at the possibility of modifying intentions, such that the goals will still be achieved but in a different way.
The Belief Desire Intention (BDI) agent paradigm provides a powerful basis for developing complex... more The Belief Desire Intention (BDI) agent paradigm provides a powerful basis for developing complex systems based on autonomous intelligent agents. These agents have, at any point in time, a set of intentions, the various tasks the agent is working on which represent the agent's multiple focus of attention. Despite its importance for intelligent behaviour, the problem of selecting which intention to progress at any point in time has received almost no attention and has been left to the programmer to resolve in an applicationdependent manner. In this paper we implement and evaluate a previous proposal for domain-independent intention selection using the notion of plan "coverage," as well as a slight variation which we predicted to perform better. We compare these with the commonly used intention selection mechanisms of First-In-First-Out (FIFO) and Round Robin (RR). We show that the coverage-based technique performs better under all circumstances, but particularly with low coverage and volatile environments. Interestingly, we found that a simple one-step look-ahead applicability check is responsible for the largest part of the improvement. This is important in that this can readily be applied to FIFO and RR, giving an extremely simple and effective mechanism to be added to existing BDI frameworks.
The composition problem involves how to coordinate a set of available modules (e.g., concrete dev... more The composition problem involves how to coordinate a set of available modules (e.g., concrete devices installed in a smart house, such as video cameras, lights, blinds, etc.) so as to implement a desired but non-existent target complex component (e.g., a complex entertainment house system). This paper summarizes the results in , by formally defining the problem within an AI context, characterizing its complexity, and identifying effective techniques to solve it. Related results are also briefly discussed.
The behavior composition problem involves the automatic synthesis of a controller able to "realiz... more The behavior composition problem involves the automatic synthesis of a controller able to "realize" (i.e., implement) a desired target behavior specification by suitably coordinating a set of already available behaviors. While the problem has been thoroughly studied, one open issue has resisted a principled solution: if the target specification is not fully realizable, is there a way to realize it "at best"? In this paper we answer positively, by showing that there exists a unique supremal realizable target behavior satisfying the specification. More importantly we give an effective procedure to compute such a target. Then, we introduce exogenous events, and show that the supremal can again be computed, though this time, into two variants, depending on the ability to observe such events.
Belief, Desire, and Intentions (BDI) agents are well suited for com- plex applications with (soft... more Belief, Desire, and Intentions (BDI) agents are well suited for com- plex applications with (soft) real-time reasoning and control re- quirements. BDI agents are adaptive in the sense that they can quickly reason and react to asynchronous events and act accord- ingly. However, BDI agents lack learning capabilities to modify their behavior when failures occur frequently. We discuss the use
Journal on Data Semantics, 2014
Lecture Notes in Computer Science, 2012
We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the agents' operational... more We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the agents' operational know-how, as defined by their libraries of abstract plans. Inspired by ATLES, a variant itself of ATL, it is possible in our logic to explicitly refer to "rational" strategies for agents developed under the Belief-Desire-Intention agent programming paradigm. This allows us to express and verify properties of BDI systems using ATL-type logical frameworks.
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Papers by Sebastian Sardina