Taking Account of the Actions of Others in Value-based Reasoning

Practical reasoning, reasoning about what actions should be chosen, is highly dependent both on the individual values of the agent concerned and on what others choose to do. Hitherto, computational models of value-based argumentation for practical reasoning have required assumptions to be made about the beliefs and preferences of other agents. Here we present a new method for taking the actions of others into account that does not require these assumptions: the only beliefs and preferences considered are those of the agent engaged in the reasoning. Our new formalism draws on utility-based approaches and expresses the reasoning in the form of arguments and objections, to enable full integration with value-based practical reasoning. We illustrate our approach by showing how value-based reasoning is modelled in two scenarios used in experimental economics, the Ultimatum Game and the Prisoner's Dilemma, and we present an evaluation of our approach in terms of these experiments. The evaluation demonstrates that our model is able to reproduce computationally the results of ethnographic experiments, serving as an encouraging validation exercise

Introduction

This chapter begins by explaining two widespread attitudes towards the methods of moral philosophy. The first common attitude is that the appropriate method for doing ethics was described by John Rawls when he formulated the reflective equilibrium method. Another common attitude is that moral philosophy has no method – anything goes in ethical theorising as long as the results are significant enough. The chapter then motivates the volume by arguing that these attitudes are not helpful. The reflective equilibrium method has its limits and yet not all ways of proceeding in ethics are equally good. For this reason, I argue that we need to be more aware of the argumentative strategies we employ in ethics. This requires being methodologically reflective and transparent and taking part in the debates about the merits and problems of different methodologies exactly in the way done in the chapters of this volume. The second half of the chapter then provides an outline of the other chapters. Here I focus on clarifying exactly how these chapters contribute to the new discussions about the methods of ethics

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

Cluelessness

Decisions, whether moral or prudential, should be guided at least in part by considerations of the consequences that would result from the various available actions. For any given action, however, the majority of its consequences are unpredictable at the time of decision. Many have worried that this leaves us, in some important sense, clueless. In this paper, I distinguish between ‘simple’ and ‘complex’ possible sources of cluelessness. In terms of this taxonomy, the majority of the existing literature on cluelessness focusses on the simple sources. I argue, contra James Lenman in particular, that these would-be sources of cluelessness are unproblematic, on the grounds that indifference-based reasoning is far less problematic than Lenman (along with many others) supposes. However, there does seem to be a genuine phenomenon of cluelessness associated with the ‘complex’ sources; here, indifference-based reasoning is inapplicable by anyone’s lights. This ‘complex problem of cluelessness’ is vivid and pressing, in particular, in the context of Effective Altruism. This motivates a more thorough examination of the precise nature of cluelessness, and the precise source of the associated phenomenology of discomfort in forced-choice situations. The latter parts of the paper make some initial explorations in those directions

An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

Character and theory of mind: an integrative approach

Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system for action prediction, and serves to inform hypotheses about agents’ beliefs and desires, which are in turn used to predict and interpret behavior

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Prosocial and antisocial children's perceptions of peers' motives for prosocial behaviours

This study investigated whether peer-nominated prosocial and antisocial children have different perceptions of the motives underlying peers' prosocial actions. Eighty-seven children, aged 10-12 years old, completed peer-nomination measures of social behaviour. On the basis of numbers of social nominations received, a subsample of 51 children (32 who were peer-nominated as 'prosocial', and 18 who were peer-nominated as 'antisocial') then recorded their perceptions of peers' motives for prosocial behaviours. Expressed motives were categorized predominantly into three categories, coinciding with Turiel's (1978) 'moral', 'conventional', and 'personal domains'. Results indicate that children's social reputation is associated with the extent to which they perceive peers' prosocial motives as 'personal' or 'moral', with more prosocial children attributing moral motives, and more antisocial children attributing personal motives. Although traditionally Turiel's domain theory has been used to understand 'antisocial' children's behaviour, the current findings suggest that 'prosocial' children's behaviour may also be related to domains of judgment