An Account of Intensional and Extensional Actions, and its Application to Belief, Nondeterministic Actions and Fallible Sensors

In general, an agent may have incomplete and inaccurate knowledge about its environment. As well, actions may not turn out as intended or may have nondeterministic effects, and sensors may on occasion give incorrect results. We present a general, qualitative approach to reasoning about action and change in such a setting. The approach is expressed as an extension to basic action theories in the situation calculus, where an agent's epistemic state is modelled by a set of situations, where each situation is assigned a non-negative integer representing its plausibility. The agent's epistemic state is updated by modifying these plausibility values after the execution of an action, taking into account the possibility of unexpected results. To this end, we consider actions to have an intensional aspect, under the control of and determined by the agent, and an extensional aspect, not directly accessible to the agent and controlled by "nature". This leads to two distinct but related related notions of belief, an extensional "bird's eye" view which models an agent's beliefs wrt actually-executed actions, and an intensional view representing beliefs from the agent's point of view. We argue that the approach is significantly more general and comprehensive than previous accounts, and leads to a unified view of failed actions and nondeterminism with respect to physical and sensing actions.

Dyadic Obligations over Complex Actions as Deontic Constraints in the Situation Calculus

With the advent of artificial agents in everyday life, it is important that these agents are guided by social norms and moral guidelines. Notions of obligation, permission, and the like have traditionally been studied in the field of Deontic Logic, where deontic assertions generally refer to what an agent should or should not do; that is they refer to actions. In Artificial Intelligence, the Situation Calculus is (arguably) the best known and most studied formalism for reasoning about action and change. In this paper, we integrate these two areas by incorporating deontic notions into Situation Calculus theories. We do this by considering deontic assertions as constraints, expressed as a set of conditionals, which apply to complex actions expressed as GOLOG programs. These constraints induce a ranking of "ideality" over possible future situations. This ranking in turn is used to guide an agent in its planning deliberation, towards a course of action that adheres best to the deontic constraints. We present a formalization that includes a wide class of (dyadic) deontic assertions, lets us distinguish prima facie from all-things-considered obligations, and particularly addresses contrary-to-duty scenarios. We furthermore present results on compiling the deontic constraints directly into the Situation Calculus action theory, so as to obtain an agent that respects the given norms, but works solely based on the standard reasoning and planning techniques.

Bayesian Execution Skill Estimation

The performance of agents in many domains with continuous action spaces depends not only on their ability to select good actions to execute, but also on their ability to execute planned actions precisely. This ability, which has been called an agent’s execution skill, is an important characteristic of an agent which can have a significant impact on their success. In this paper, we address the problem of estimating the execution skill of an agent given observations of that agent acting in a domain. Each observation includes the executed action and a description of the state in which the action was executed and the reward received, but notably excludes the action that the agent intended to execute. We previously introduced this problem and demonstrated that estimating an agent’s execution skill is possible under certain conditions. Our previous method focused entirely on the reward that the agent received from executed actions and assumed that the agent was able to select the optimal action for each state. This paper addresses the execution skill estimation problem from an entirely different perspective, focusing instead on the action that was executed. We present a Bayesian framework for reasoning about action observations and show that it is able to outperform previous methods under the same conditions. We also show that the flexibility of this framework allows it to be applied in settings where the previous limiting assumptions are not met. The success of the proposed method is demonstrated experimentally in a toy domain as well as the domain of computational billiards.

Kantian ethics

Kantian ethics originates in the ethical writings of Immanuel Kant (1724–1804), which remain the most influential attempt to vindicate universal ethical principles that respect the dignity and equality of human beings without presupposing theological claims or a metaphysical conception of the good. Kant’s systematic, critical philosophy centres on an account of reasoning about action, which he uses to justify principles of duty and virtue, a liberal and republican conception of justice with cosmopolitan scope, and an account of the relationship between morality and hope. Numerous contemporary writers also advance views of ethics which they, and their critics, think of as Kantian. However, some contemporary work is remote from Kant’s philosophy on fundamental matters such as human freedom and reasoning about action. It converges with Kant’s ethics in claiming that we lack a substantive account of the good (so that teleological or consequentialist ethics are impossible), in taking a strong view of the equality of moral agents and the importance of universal principles of duty which spell out what it is to respect them, and in stressing an account of justice and rights with cosmopolitan scope. Both Kant’s ethics and contemporary Kantian ethics have been widely criticized for preoccupation with rules and duties, and for lack of concern with virtues, happiness or personal relationships. However, these criticisms may apply more to recent Kantian ethics than to Kant’s own ethics.

Kantian ethics

Kantian ethics originates in the ethical writings of Immanuel Kant (1724–1804), which remain the most influential attempt to vindicate universal ethical principles that respect the dignity and equality of human beings without relying on theological claims or a metaphysical conception of the good. Kant’s systematic, critical philosophy centres on an account of reasoning about action, that is practical reasoning, which he uses to derive principles of duty and virtue, a liberal and republican conception of justice with cosmopolitan scope, and an account of the relationship between morality and hope. Numerous contemporary writers also advance views of ethics which they, and their critics, think of as Kantian. However, some contemporary work is remote from Kant’s philosophy on fundamental matters such as human freedom and practical reason. It converges with Kant’s ethics in claiming that we lack a substantive account of the good (so that teleological or consequentialist ethics are impossible), in taking a strong view of the fundamental equality of moral agents and the importance of universal principles of duty which spell out what it is to respect them, and in stressing an account of justice and rights with cosmopolitan scope. Both Kant’s ethics and contemporary Kantian ethics have been widely criticized for preoccupation with rules and duties, and for lack of concern with virtues, happiness or personal relationships. However, these criticisms may apply more to certain strands within modern Kantian ethics than to Kant’s own ethics.

Experimenting with robotic intra-logistics domains

We introduce theasprilo1framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return,aspriloallows users to study alternative solutions as regards effectiveness and scalability. Althoughasprilorelies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely,aspriloconsists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.

Temporal Answer Set Programming on Finite Traces

In this paper, we introduce an alternative approach to Temporal Answer Set Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for finite traces. This approach allows us to even out the expressiveness of TEL over infinite traces with the computational capacity of (incremental) Answer Set Programming (ASP). Also, we argue that finite traces are more natural when reasoning about action and change. As a result, our approach is readily implementable via multi-shot ASP systems and benefits from an extension of ASP's full-fledged input language with temporal operators. This includes future as well as past operators whose combination offers a rich temporal modeling language. For computation, we identify the class of temporal logic programs and prove that it constitutes a normal form for our approach. Finally, we outline two implementations, a generic one and an extension of the ASP systemclingo.Under consideration for publication in Theory and Practice of Logic Programming (TPLP)

A Tool for Spatial Reasoning in XML Documents

In this paper, we study the ramification problem in the setting of spatial xml data. Standard solutions from the literature on reasoning about action are inadequate because they cannot capture integrity constraints in spatial data. In this paper, we provide a solution to the ramification problem based on situation calculus. We present a tool that connects the theoretical results with the practical considerations, by producing the User Interface in C# in order to address the ramification problem in spatial XML file in specific time period. a