scholarly journals Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

2012 ◽  
Vol 367 (1603) ◽  
pp. 2723-2732 ◽  
Author(s):  
Jackie Chappell ◽  
Nick Hawes
Keyword(s):  
Artificial Intelligence ◽  
Associative Learning ◽  
Animal Cognition ◽  
Learning Processes ◽  
Simulation Environment ◽  
Physical Cognition ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
Simple Set ◽  
Complex Forms

Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended.

scholarly journals What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

scholarly journals What can associative learning do for planning?

2018 ◽  
Vol 5 (11) ◽  
pp. 180778 ◽  
Author(s):  
Johan Lind
Keyword(s):  
Artificial Intelligence ◽  
Associative Learning ◽  
Animal Cognition ◽  
Empirical Studies ◽  
Learning Model ◽  
Learning Paradox ◽  
Flexible Planning ◽  
A Current ◽  
Rule Out ◽  
Theoretical Developments

There is a new associative learning paradox. The power of associative learning for producing flexible behaviour in non-human animals is downplayed or ignored by researchers in animal cognition, whereas artificial intelligence research shows that associative learning models can beat humans in chess. One phenomenon in which associative learning often is ruled out as an explanation for animal behaviour is flexible planning. However, planning studies have been criticized and questions have been raised regarding both methodological validity and interpretations of results. Due to the power of associative learning and the uncertainty of what causes planning behaviour in non-human animals, I explored what associative learning can do for planning. A previously published sequence learning model which combines Pavlovian and instrumental conditioning was used to simulate two planning studies, namely Mulcahy & Call 2006 ‘Apes save tools for future use.’ Science 312 , 1038–1040 and Kabadayi & Osvath 2017 ‘Ravens parallel great apes in flexible planning for tool-use and bartering.’ Science 357 , 202–204. Simulations show that behaviour matching current definitions of flexible planning can emerge through associative learning. Through conditioned reinforcement, the learning model gives rise to planning behaviour by learning that a behaviour towards a current stimulus will produce high value food at a later stage; it can make decisions about future states not within current sensory scope. The simulations tracked key patterns both between and within studies. It is concluded that one cannot rule out that these studies of flexible planning in apes and corvids can be completely accounted for by associative learning. Future empirical studies of flexible planning in non-human animals can benefit from theoretical developments within artificial intelligence and animal learning.

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

2020 ◽  
Author(s):  
Jens Claßen ◽  
James Delgrande
Keyword(s):  
Artificial Intelligence ◽  
Deontic Logic ◽  
Action Theory ◽  
Prima Facie ◽  
Situation Calculus ◽  
Artificial Agents ◽  
Planning Techniques ◽  
Course Of Action ◽  
Reasoning About Action ◽  
The Given

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.

Artificial Intelligence and Education

2011 ◽  
pp. 138-143
Author(s):  
Eduardo Sánchez ◽  
Manuel Lama
Keyword(s):  
Artificial Intelligence ◽  
Work Environment ◽  
Learning Strategies ◽  
Learning Processes ◽  
Educational Systems ◽  
Graduate Degrees ◽  
Life Long Learning ◽  
Key Factor ◽  
State Of Art ◽  
Educational Costs

Governments and institutions are facing the new demands of a rapidly changing society. Among many significant trends, some facts should be considered (Silverstein, 2006): (1) the increment of number and type of students; and (2) the limitations imposed by educational costs and course schedules. About the former, the need of a continuous update of knowledge and competences in an evolving work environment requires life-long learning solutions. An increasing number of young adults are returning to classrooms in order to finish their graduate degrees or attend postgraduate programs to achieve an specialization on a certain domain. About the later, due to the emergence of new types of students, budget constraints and schedule conflicts appear. Workers and immigrants, for instance, are relevant groups for which educational costs and job incompatible schedules could be the key factor to register into a course or to give up a program after investing time and effort on it. In order to solve the needs derived from this social context, new educational approaches should be proposed: (1) to improve and extend the online learning courses, which would reduce student costs and allows to cover the educational needs of a higher number of students, and (2) to automate learning processes, then reducing teacher costs and providing a more personalized educational experience anytime, anywhere. As a result of this context, in the last decade an increasing interest on applying computer technologies in the field of Education has been observed. On this regard, the paradigms of the Artificial Intelligence (AI) field are attracting an special attention to solve the issues derived from the introduction of computers as supporting resources of different learning strategies. In this paper we review the state-of-art of the application of Artificial Intelligence techniques in the field of Education, focusing on (1) the most popular educational tools based on AI, and (2) the most relevant AI techniques applied on the development of intelligent educational systems.

Modulation of a Na/K electrogenic pump as new molecular mechanism underlying non-associative learning processes in invertebrates

1995 ◽  
Vol 6 (SUPPLEMENT 1) ◽  
pp. 150
Author(s):  
M Garcia-Gil ◽  
R Mozzachiodi ◽  
R Scuri ◽  
M L Zaccardi ◽  
M Brunelli
Keyword(s):  
Associative Learning ◽  
Molecular Mechanism ◽  
Learning Processes ◽  
Electrogenic Pump
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